diff --git a/2-second-project/tdt/DataViz.ipynb b/2-second-project/tdt/DataViz.ipynb index 93c2ec2..ad694e8 100644 --- a/2-second-project/tdt/DataViz.ipynb +++ b/2-second-project/tdt/DataViz.ipynb @@ -2,23 +2,10 @@ "cells": [ { "cell_type": "code", -<<<<<<< HEAD "execution_count": 1, - "id": "a33b4ae2", -======= - "execution_count": null, - "id": "39df48da", ->>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 + "id": "de9c6d92", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 59%|█████▉ | 453/768 [00:40<00:23, 13.66it/s]" - ] - } - ], + "outputs": [], "source": [ "import os\n", "\n", @@ -29,7 +16,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "bfcb55c8", + "id": "9a0834ed", "metadata": {}, "outputs": [], "source": [ @@ -41,7 +28,26 @@ { "cell_type": "code", "execution_count": 3, - "id": "7bb04d71", + "id": "68a72718", + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "def pplot(dd):\n", + " x = dd.shape[0]\n", + " fix = int(x/3)+1\n", + " fiy = 3\n", + " fig, axs = plt.subplots(fix, fiy, figsize=(3*fiy, 9*fix))\n", + " \n", + " for i in range(x):\n", + " axs[int(i/3)][i%3].plot(dd[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0ef04cbe", "metadata": { "tags": [] }, @@ -55,11 +61,7 @@ " \n", " dic_data = []\n", " \n", -<<<<<<< HEAD " for p in tqdm(glob(glob_path)):\n", -======= - " for p in tqdm(glob('/opt/iui-datarelease3-sose2021/*.csv')):\n", ->>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 " path = p\n", " filename = path.split('/')[-1].split('.')[0]\n", " splitname = filename.split('_')\n", @@ -67,21 +69,11 @@ " if (user_filter):\n", " if (user != user_filter):\n", " continue\n", -<<<<<<< HEAD " scenario = splitname[1][len('Scenario'):]\n", " heightnorm = splitname[2][len('HeightNormalization'):] == 'True'\n", " armnorm = splitname[3][len('ArmNormalization'):] == 'True'\n", " rep = int(splitname[4][len('Repetition'):])\n", " session = int(splitname[5][len('Session'):])\n", -======= - " scenario = filename.split('_')[1][len('Scenario'):]\n", - " heightnorm = filename.split('_')[2][len('HeightNormalization'):] == 'True'\n", - " armnorm = filename.split('_')[3][len('ArmNormalization'):] == 'True'\n", - " rep = int(filename.split('.')[0].split('_')[4][len('Repetition'):])\n", - " session = filename.split('_')[5][len('Session'):]\n", - " session = session.split('.')[0]\n", - " \n", ->>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 " data = pd.read_csv(path)\n", " dic_data.append(\n", " {\n", @@ -93,7 +85,6 @@ " 'rep': rep,\n", " 'session': session,\n", " 'data': data \n", - " \n", " }\n", " )\n", " return dic_data" @@ -101,8 +92,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "9adc333e", + "execution_count": 5, + "id": "26ab08b9", "metadata": {}, "outputs": [], "source": [ @@ -116,8 +107,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "dfc32785", + "execution_count": 6, + "id": "06befbd4", "metadata": {}, "outputs": [], "source": [ @@ -131,8 +122,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "09a66223", + "execution_count": 7, + "id": "05bfe750", "metadata": {}, "outputs": [ { @@ -141,8 +132,8 @@ "text": [ "Loading data...\n", "../data.pickle found...\n", - "CPU times: user 512 ms, sys: 2.47 s, total: 2.98 s\n", - "Wall time: 2.98 s\n" + "CPU times: user 597 ms, sys: 2.34 s, total: 2.94 s\n", + "Wall time: 2.94 s\n" ] } ], @@ -167,8 +158,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "07df007d", + "execution_count": 14, + "id": "f0a56d84", "metadata": { "tags": [] }, @@ -177,7 +168,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "192\n" + "SYY : 96\n", + "SYN : 96\n", + "SNY : 96\n", + "SNN : 96\n", + "JYY : 96\n", + "JYN : 96\n", + "JNY : 96\n", + "JNN : 96\n" ] }, { @@ -229,121 +227,121 @@ " 0\n", " 0\n", " 0\n", - " 7\n", + " 1\n", " SortingBlocksScene\n", " True\n", " True\n", - " 1\n", + " 2\n", + " Low\n", " High\n", - " High\n", - " -0.089895\n", + " 0.681254\n", " ...\n", - " 307.9409\n", - " 320.01970\n", - " 195.1051\n", - " 0.016631\n", - " 1.106319\n", - " 0.430588\n", - " 310.8786\n", - " 343.57170\n", - " 185.1874\n", - " 1\n", + " 302.7715\n", + " 36.99136\n", + " 137.2513\n", + " 0.713257\n", + " 0.870861\n", + " 1.073421\n", + " 316.0526\n", + " 40.26445\n", + " 156.0836\n", + " 2\n", " \n", " \n", " 1\n", " 1\n", " 1\n", - " 7\n", + " 1\n", " SortingBlocksScene\n", " True\n", " True\n", - " 1\n", + " 2\n", + " Low\n", " High\n", - " High\n", - " -0.089738\n", + " 0.681238\n", " ...\n", - " 308.1140\n", - " 320.34520\n", - " 195.5152\n", - " 0.007256\n", - " 1.145476\n", - " 0.423464\n", - " 311.1056\n", - " 343.74560\n", - " 185.6717\n", - " 1\n", + " 302.6280\n", + " 37.42541\n", + " 141.2726\n", + " 0.714441\n", + " 0.864941\n", + " 1.075697\n", + " 315.9267\n", + " 40.45845\n", + " 156.8290\n", + " 2\n", " \n", " \n", " 2\n", " 2\n", " 2\n", - " 7\n", + " 1\n", " SortingBlocksScene\n", " True\n", " True\n", - " 1\n", + " 2\n", + " Low\n", " High\n", - " High\n", - " -0.089347\n", + " 0.681438\n", " ...\n", - " 308.2395\n", - " 320.51180\n", - " 195.7206\n", - " 0.007426\n", - " 1.144992\n", - " 0.423223\n", - " 311.2307\n", - " 343.79710\n", - " 185.9125\n", - " 1\n", + " 302.4141\n", + " 37.24395\n", + " 144.9683\n", + " 0.714923\n", + " 0.863987\n", + " 1.076074\n", + " 315.5687\n", + " 40.50327\n", + " 157.4256\n", + " 2\n", " \n", " \n", " 3\n", " 3\n", " 3\n", - " 7\n", + " 1\n", " SortingBlocksScene\n", " True\n", " True\n", - " 1\n", + " 2\n", + " Low\n", " High\n", - " High\n", - " -0.088938\n", + " 0.681680\n", " ...\n", - " 308.3445\n", - " 320.66750\n", - " 195.9804\n", - " 0.007635\n", - " 1.144509\n", - " 0.423137\n", - " 311.3370\n", - " 343.84570\n", - " 186.1985\n", - " 1\n", + " 302.1731\n", + " 36.79346\n", + " 148.4191\n", + " 0.715369\n", + " 0.863188\n", + " 1.076337\n", + " 315.0991\n", + " 40.50356\n", + " 157.8922\n", + " 2\n", " \n", " \n", " 4\n", " 4\n", " 4\n", - " 7\n", + " 1\n", " SortingBlocksScene\n", " True\n", " True\n", - " 1\n", + " 2\n", + " Low\n", " High\n", - " High\n", - " -0.088715\n", + " 0.681469\n", " ...\n", - " 308.5142\n", - " 320.82160\n", - " 196.0655\n", - " 0.007918\n", - " 1.144068\n", - " 0.422891\n", - " 311.3891\n", - " 343.80610\n", - " 186.5528\n", - " 1\n", + " 301.9409\n", + " 36.35692\n", + " 151.8196\n", + " 0.715776\n", + " 0.862477\n", + " 1.076571\n", + " 314.6485\n", + " 40.52340\n", + " 158.2699\n", + " 2\n", " \n", " \n", " ...\n", @@ -370,259 +368,291 @@ " ...\n", " \n", " \n", - " 2020\n", - " 2020\n", - " 2020\n", - " 7\n", + " 1257\n", + " 1257\n", + " 1257\n", + " 1\n", " SortingBlocksScene\n", " True\n", " True\n", - " 1\n", - " High\n", - " High\n", - " 1.067835\n", + " 2\n", + " Low\n", + " Low\n", + " 0.153791\n", " ...\n", - " 334.3178\n", - " 16.43365\n", - " 271.1187\n", - " 1.245457\n", - " 0.619399\n", - " -0.084547\n", - " 324.5029\n", - " 26.11914\n", - " 271.3724\n", - " 1\n", + " 349.6221\n", + " 280.89230\n", + " 141.6955\n", + " 0.292764\n", + " 0.635226\n", + " 0.777857\n", + " 348.3104\n", + " 289.75490\n", + " 125.4430\n", + " 2\n", " \n", " \n", - " 2021\n", - " 2021\n", - " 2021\n", - " 7\n", + " 1258\n", + " 1258\n", + " 1258\n", + " 1\n", " SortingBlocksScene\n", " True\n", " True\n", - " 1\n", - " High\n", - " High\n", - " 1.076106\n", + " 2\n", + " Low\n", + " Low\n", + " 0.161396\n", " ...\n", - " 334.1573\n", - " 15.85253\n", - " 271.2372\n", - " 1.260631\n", - " 0.606978\n", - " -0.078567\n", - " 324.4428\n", - " 25.80618\n", - " 271.3020\n", - " 1\n", + " 346.6641\n", + " 283.53940\n", + " 168.4210\n", + " 0.352627\n", + " 0.598427\n", + " 0.735074\n", + " 347.6538\n", + " 290.61270\n", + " 126.2281\n", + " 2\n", " \n", " \n", - " 2022\n", - " 2022\n", - " 2022\n", - " 7\n", + " 1259\n", + " 1259\n", + " 1259\n", + " 1\n", " SortingBlocksScene\n", " True\n", " True\n", - " 1\n", - " High\n", - " High\n", - " 1.085397\n", + " 2\n", + " Low\n", + " Low\n", + " 0.169369\n", " ...\n", - " 334.0576\n", - " 15.55901\n", - " 271.0670\n", - " 1.269063\n", - " 0.599918\n", - " -0.075514\n", - " 324.3901\n", - " 25.65784\n", - " 270.9597\n", - " 1\n", + " 346.6641\n", + " 283.53940\n", + " 168.4210\n", + " 0.353179\n", + " 0.598251\n", + " 0.735154\n", + " 347.6538\n", + " 290.61270\n", + " 126.2281\n", + " 2\n", " \n", " \n", - " 2023\n", - " 2023\n", - " 2023\n", - " 7\n", + " 1260\n", + " 1260\n", + " 1260\n", + " 1\n", " SortingBlocksScene\n", " True\n", " True\n", - " 1\n", - " High\n", - " High\n", - " 1.096437\n", + " 2\n", + " Low\n", + " Low\n", + " 0.177724\n", " ...\n", - " 333.9445\n", - " 15.22374\n", - " 270.9147\n", - " 1.278922\n", - " 0.592047\n", - " -0.071804\n", - " 324.3320\n", - " 25.47795\n", - " 270.6435\n", - " 1\n", + " 346.6641\n", + " 283.53940\n", + " 168.4210\n", + " 0.353123\n", + " 0.598169\n", + " 0.735184\n", + " 347.6538\n", + " 290.61270\n", + " 126.2281\n", + " 2\n", " \n", " \n", - " 2024\n", - " 2024\n", - " 2024\n", - " 7\n", + " 1261\n", + " 1261\n", + " 1261\n", + " 1\n", " SortingBlocksScene\n", " True\n", " True\n", - " 1\n", - " High\n", - " High\n", - " 1.106890\n", + " 2\n", + " Low\n", + " Low\n", + " 0.186001\n", " ...\n", - " 333.7580\n", - " 14.87231\n", - " 270.0383\n", - " 1.293765\n", - " 0.578599\n", - " -0.066561\n", - " 324.2077\n", - " 25.11721\n", - " 269.5140\n", - " 1\n", + " 340.3340\n", + " 286.02930\n", + " 195.8516\n", + " 0.403420\n", + " 0.560423\n", + " 0.685445\n", + " 344.4067\n", + " 296.86830\n", + " 140.0699\n", + " 2\n", " \n", " \n", "\n", - "

2025 rows × 346 columns

\n", + "

1262 rows × 346 columns

\n", "" ], "text/plain": [ " Unnamed: 0 FrameID participantID Scenario \\\n", - "0 0 0 7 SortingBlocksScene \n", - "1 1 1 7 SortingBlocksScene \n", - "2 2 2 7 SortingBlocksScene \n", - "3 3 3 7 SortingBlocksScene \n", - "4 4 4 7 SortingBlocksScene \n", + "0 0 0 1 SortingBlocksScene \n", + "1 1 1 1 SortingBlocksScene \n", + "2 2 2 1 SortingBlocksScene \n", + "3 3 3 1 SortingBlocksScene \n", + "4 4 4 1 SortingBlocksScene \n", "... ... ... ... ... \n", - "2020 2020 2020 7 SortingBlocksScene \n", - "2021 2021 2021 7 SortingBlocksScene \n", - "2022 2022 2022 7 SortingBlocksScene \n", - "2023 2023 2023 7 SortingBlocksScene \n", - "2024 2024 2024 7 SortingBlocksScene \n", + "1257 1257 1257 1 SortingBlocksScene \n", + "1258 1258 1258 1 SortingBlocksScene \n", + "1259 1259 1259 1 SortingBlocksScene \n", + "1260 1260 1260 1 SortingBlocksScene \n", + "1261 1261 1261 1 SortingBlocksScene \n", "\n", " HeightNormalization ArmNormalization Repetition \\\n", - "0 True True 1 \n", - "1 True True 1 \n", - "2 True True 1 \n", - "3 True True 1 \n", - "4 True True 1 \n", + "0 True True 2 \n", + "1 True True 2 \n", + "2 True True 2 \n", + "3 True True 2 \n", + "4 True True 2 \n", "... ... ... ... \n", - "2020 True True 1 \n", - "2021 True True 1 \n", - "2022 True True 1 \n", - "2023 True True 1 \n", - "2024 True True 1 \n", + "1257 True True 2 \n", + "1258 True True 2 \n", + "1259 True True 2 \n", + "1260 True True 2 \n", + "1261 True True 2 \n", "\n", " LeftHandTrackingAccuracy RightHandTrackingAccuracy \\\n", - "0 High High \n", - "1 High High \n", - "2 High High \n", - "3 High High \n", - "4 High High \n", + "0 Low High \n", + "1 Low High \n", + "2 Low High \n", + "3 Low High \n", + "4 Low High \n", "... ... ... \n", - "2020 High High \n", - "2021 High High \n", - "2022 High High \n", - "2023 High High \n", - "2024 High High \n", + "1257 Low Low \n", + "1258 Low Low \n", + "1259 Low Low \n", + "1260 Low Low \n", + "1261 Low Low \n", "\n", " CenterEyeAnchor_pos_X ... right_Hand_RingTip_euler_X \\\n", - "0 -0.089895 ... 307.9409 \n", - "1 -0.089738 ... 308.1140 \n", - "2 -0.089347 ... 308.2395 \n", - "3 -0.088938 ... 308.3445 \n", - "4 -0.088715 ... 308.5142 \n", + "0 0.681254 ... 302.7715 \n", + "1 0.681238 ... 302.6280 \n", + "2 0.681438 ... 302.4141 \n", + "3 0.681680 ... 302.1731 \n", + "4 0.681469 ... 301.9409 \n", "... ... ... ... \n", - "2020 1.067835 ... 334.3178 \n", - "2021 1.076106 ... 334.1573 \n", - "2022 1.085397 ... 334.0576 \n", - "2023 1.096437 ... 333.9445 \n", - "2024 1.106890 ... 333.7580 \n", + "1257 0.153791 ... 349.6221 \n", + "1258 0.161396 ... 346.6641 \n", + "1259 0.169369 ... 346.6641 \n", + "1260 0.177724 ... 346.6641 \n", + "1261 0.186001 ... 340.3340 \n", "\n", " right_Hand_RingTip_euler_Y right_Hand_RingTip_euler_Z \\\n", - "0 320.01970 195.1051 \n", - "1 320.34520 195.5152 \n", - "2 320.51180 195.7206 \n", - "3 320.66750 195.9804 \n", - "4 320.82160 196.0655 \n", + "0 36.99136 137.2513 \n", + "1 37.42541 141.2726 \n", + "2 37.24395 144.9683 \n", + "3 36.79346 148.4191 \n", + "4 36.35692 151.8196 \n", "... ... ... \n", - "2020 16.43365 271.1187 \n", - "2021 15.85253 271.2372 \n", - "2022 15.55901 271.0670 \n", - "2023 15.22374 270.9147 \n", - "2024 14.87231 270.0383 \n", + "1257 280.89230 141.6955 \n", + "1258 283.53940 168.4210 \n", + "1259 283.53940 168.4210 \n", + "1260 283.53940 168.4210 \n", + "1261 286.02930 195.8516 \n", "\n", " right_Hand_PinkyTip_pos_X right_Hand_PinkyTip_pos_Y \\\n", - "0 0.016631 1.106319 \n", - "1 0.007256 1.145476 \n", - "2 0.007426 1.144992 \n", - "3 0.007635 1.144509 \n", - "4 0.007918 1.144068 \n", + "0 0.713257 0.870861 \n", + "1 0.714441 0.864941 \n", + "2 0.714923 0.863987 \n", + "3 0.715369 0.863188 \n", + "4 0.715776 0.862477 \n", "... ... ... \n", - "2020 1.245457 0.619399 \n", - "2021 1.260631 0.606978 \n", - "2022 1.269063 0.599918 \n", - "2023 1.278922 0.592047 \n", - "2024 1.293765 0.578599 \n", + "1257 0.292764 0.635226 \n", + "1258 0.352627 0.598427 \n", + "1259 0.353179 0.598251 \n", + "1260 0.353123 0.598169 \n", + "1261 0.403420 0.560423 \n", "\n", " right_Hand_PinkyTip_pos_Z right_Hand_PinkyTip_euler_X \\\n", - "0 0.430588 310.8786 \n", - "1 0.423464 311.1056 \n", - "2 0.423223 311.2307 \n", - "3 0.423137 311.3370 \n", - "4 0.422891 311.3891 \n", + "0 1.073421 316.0526 \n", + "1 1.075697 315.9267 \n", + "2 1.076074 315.5687 \n", + "3 1.076337 315.0991 \n", + "4 1.076571 314.6485 \n", "... ... ... \n", - "2020 -0.084547 324.5029 \n", - "2021 -0.078567 324.4428 \n", - "2022 -0.075514 324.3901 \n", - "2023 -0.071804 324.3320 \n", - "2024 -0.066561 324.2077 \n", + "1257 0.777857 348.3104 \n", + "1258 0.735074 347.6538 \n", + "1259 0.735154 347.6538 \n", + "1260 0.735184 347.6538 \n", + "1261 0.685445 344.4067 \n", "\n", " right_Hand_PinkyTip_euler_Y right_Hand_PinkyTip_euler_Z Session \n", - "0 343.57170 185.1874 1 \n", - "1 343.74560 185.6717 1 \n", - "2 343.79710 185.9125 1 \n", - "3 343.84570 186.1985 1 \n", - "4 343.80610 186.5528 1 \n", + "0 40.26445 156.0836 2 \n", + "1 40.45845 156.8290 2 \n", + "2 40.50327 157.4256 2 \n", + "3 40.50356 157.8922 2 \n", + "4 40.52340 158.2699 2 \n", "... ... ... ... \n", - "2020 26.11914 271.3724 1 \n", - "2021 25.80618 271.3020 1 \n", - "2022 25.65784 270.9597 1 \n", - "2023 25.47795 270.6435 1 \n", - "2024 25.11721 269.5140 1 \n", + "1257 289.75490 125.4430 2 \n", + "1258 290.61270 126.2281 2 \n", + "1259 290.61270 126.2281 2 \n", + "1260 290.61270 126.2281 2 \n", + "1261 296.86830 140.0699 2 \n", "\n", - "[2025 rows x 346 columns]" + "[1262 rows x 346 columns]" ] }, - "execution_count": 7, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fil_dic_data = []\n", + "# Categorized Data\n", + "cdata = dict() \n", + "# Sorting, HeightNorm, ArmNorm\n", + "cdata['SYY'] = list() \n", + "cdata['SYN'] = list() \n", + "cdata['SNY'] = list() \n", + "cdata['SNN'] = list() \n", + "\n", + "# Jenga, HeightNorm, ArmNorm\n", + "cdata['JYY'] = list() \n", + "cdata['JYN'] = list() \n", + "cdata['JNY'] = list() \n", + "cdata['JNN'] = list() \n", "for d in dic_data:\n", " if d['scenario'] == 'Sorting':\n", - " if d['heightnorm'] == d['armnorm']:\n", - " fil_dic_data.append(d)\n", + " if d['heightnorm']:\n", + " if d['armnorm']:\n", + " cdata['SYY'].append(d)\n", + " else:\n", + " cdata['SYN'].append(d)\n", + " else:\n", + " if d['armnorm']:\n", + " cdata['SNY'].append(d)\n", + " else:\n", + " cdata['SNN'].append(d)\n", + " elif d['scenario'] == 'Jenga':\n", + " if d['heightnorm']:\n", + " if d['armnorm']:\n", + " cdata['JYY'].append(d)\n", + " else:\n", + " cdata['JYN'].append(d)\n", + " else:\n", + " if d['armnorm']:\n", + " cdata['JNY'].append(d)\n", + " else:\n", + " cdata['JNN'].append(d)\n", "\n", - "print(len(fil_dic_data))\n", - "test_entry = fil_dic_data[15].copy()\n", + "for k,v in cdata.items():\n", + " print(k,': ',len(v))\n", + "test_entry = pickle.loads(pickle.dumps(cdata['SYY'][8]))\n", "test_entry['data']" ] }, { "cell_type": "code", - "execution_count": 81, - "id": "8d956063", + "execution_count": 15, + "id": "7774192a", "metadata": { "tags": [] }, @@ -675,122 +705,122 @@ " \n", " 0\n", " 0\n", + " Low\n", " High\n", - " High\n", - " -0.089895\n", - " 1.755665\n", - " 0.234344\n", - " 9.332605\n", - " 5.018498\n", - " 358.49900\n", - " -0.316633\n", + " 0.681254\n", + " 1.611774\n", + " 0.702683\n", + " 7.490442\n", + " 348.7060\n", + " 344.879200\n", + " 0.679794\n", " ...\n", - " 0.446973\n", - " 307.9409\n", - " 320.01970\n", - " 195.1051\n", - " 0.016631\n", - " 1.106319\n", - " 0.430588\n", - " 310.8786\n", - " 343.57170\n", - " 185.1874\n", + " 1.086929\n", + " 302.7715\n", + " 36.99136\n", + " 137.2513\n", + " 0.713257\n", + " 0.870861\n", + " 1.073421\n", + " 316.0526\n", + " 40.26445\n", + " 156.0836\n", " \n", " \n", " 1\n", " 1\n", + " Low\n", " High\n", - " High\n", - " -0.089738\n", - " 1.755732\n", - " 0.234542\n", - " 9.338191\n", - " 5.061474\n", - " 358.47840\n", - " -0.300754\n", + " 0.681238\n", + " 1.611911\n", + " 0.702356\n", + " 7.481093\n", + " 348.6785\n", + " 344.882700\n", + " 0.679784\n", " ...\n", - " 0.440061\n", - " 308.1140\n", - " 320.34520\n", - " 195.5152\n", - " 0.007256\n", - " 1.145476\n", - " 0.423464\n", - " 311.1056\n", - " 343.74560\n", - " 185.6717\n", + " 1.092474\n", + " 302.6280\n", + " 37.42541\n", + " 141.2726\n", + " 0.714441\n", + " 0.864941\n", + " 1.075697\n", + " 315.9267\n", + " 40.45845\n", + " 156.8290\n", " \n", " \n", " 2\n", " 2\n", + " Low\n", " High\n", - " High\n", - " -0.089347\n", - " 1.755780\n", - " 0.234738\n", - " 9.343684\n", - " 5.115413\n", - " 358.46080\n", - " -0.300725\n", + " 0.681438\n", + " 1.611861\n", + " 0.702440\n", + " 7.484574\n", + " 348.6573\n", + " 344.879200\n", + " 0.680203\n", " ...\n", - " 0.439948\n", - " 308.2395\n", - " 320.51180\n", - " 195.7206\n", - " 0.007426\n", - " 1.144992\n", - " 0.423223\n", - " 311.2307\n", - " 343.79710\n", - " 185.9125\n", + " 1.095202\n", + " 302.4141\n", + " 37.24395\n", + " 144.9683\n", + " 0.714923\n", + " 0.863987\n", + " 1.076074\n", + " 315.5687\n", + " 40.50327\n", + " 157.4256\n", " \n", " \n", " 3\n", " 3\n", + " Low\n", " High\n", - " High\n", - " -0.088938\n", - " 1.755686\n", - " 0.234353\n", - " 9.335765\n", - " 5.173394\n", - " 358.46820\n", - " -0.300701\n", + " 0.681680\n", + " 1.611776\n", + " 0.702397\n", + " 7.490453\n", + " 348.6290\n", + " 344.880700\n", + " 0.680205\n", " ...\n", - " 0.439995\n", - " 308.3445\n", - " 320.66750\n", - " 195.9804\n", - " 0.007635\n", - " 1.144509\n", - " 0.423137\n", - " 311.3370\n", - " 343.84570\n", - " 186.1985\n", + " 1.097335\n", + " 302.1731\n", + " 36.79346\n", + " 148.4191\n", + " 0.715369\n", + " 0.863188\n", + " 1.076337\n", + " 315.0991\n", + " 40.50356\n", + " 157.8922\n", " \n", " \n", " 4\n", " 4\n", + " Low\n", " High\n", - " High\n", - " -0.088715\n", - " 1.755643\n", - " 0.234471\n", - " 9.326243\n", - " 5.247888\n", - " 358.46250\n", - " -0.300564\n", + " 0.681469\n", + " 1.611685\n", + " 0.702336\n", + " 7.495254\n", + " 348.6104\n", + " 344.883500\n", + " 0.680203\n", " ...\n", - " 0.439725\n", - " 308.5142\n", - " 320.82160\n", - " 196.0655\n", - " 0.007918\n", - " 1.144068\n", - " 0.422891\n", - " 311.3891\n", - " 343.80610\n", - " 186.5528\n", + " 1.099373\n", + " 301.9409\n", + " 36.35692\n", + " 151.8196\n", + " 0.715776\n", + " 0.862477\n", + " 1.076571\n", + " 314.6485\n", + " 40.52340\n", + " 158.2699\n", " \n", " \n", " ...\n", @@ -817,252 +847,252 @@ " ...\n", " \n", " \n", - " 2020\n", - " 2020\n", - " High\n", - " High\n", - " 1.067835\n", - " 1.149886\n", - " 0.087708\n", - " 57.752440\n", - " 88.466320\n", - " 13.97530\n", - " 0.684950\n", + " 1257\n", + " 1257\n", + " Low\n", + " Low\n", + " 0.153791\n", + " 1.242905\n", + " 0.591736\n", + " 48.695290\n", + " 335.0239\n", + " 0.940457\n", + " 0.062155\n", " ...\n", - " -0.064587\n", - " 334.3178\n", - " 16.43365\n", - " 271.1187\n", - " 1.245457\n", - " 0.619399\n", - " -0.084547\n", - " 324.5029\n", - " 26.11914\n", - " 271.3724\n", + " 0.792824\n", + " 349.6221\n", + " 280.89230\n", + " 141.6955\n", + " 0.292764\n", + " 0.635226\n", + " 0.777857\n", + " 348.3104\n", + " 289.75490\n", + " 125.4430\n", " \n", " \n", - " 2021\n", - " 2021\n", - " High\n", - " High\n", - " 1.076106\n", - " 1.142307\n", - " 0.086917\n", - " 58.376460\n", - " 87.832810\n", - " 13.81248\n", - " 0.696102\n", + " 1258\n", + " 1258\n", + " Low\n", + " Low\n", + " 0.161396\n", + " 1.254340\n", + " 0.587556\n", + " 47.867380\n", + " 334.4086\n", + " 0.544523\n", + " 0.119903\n", " ...\n", - " -0.058271\n", - " 334.1573\n", - " 15.85253\n", - " 271.2372\n", - " 1.260631\n", - " 0.606978\n", - " -0.078567\n", - " 324.4428\n", - " 25.80618\n", - " 271.3020\n", + " 0.760241\n", + " 346.6641\n", + " 283.53940\n", + " 168.4210\n", + " 0.352627\n", + " 0.598427\n", + " 0.735074\n", + " 347.6538\n", + " 290.61270\n", + " 126.2281\n", " \n", " \n", - " 2022\n", - " 2022\n", - " High\n", - " High\n", - " 1.085397\n", - " 1.135880\n", - " 0.086078\n", - " 59.298770\n", - " 87.606250\n", - " 13.54412\n", - " 0.702457\n", + " 1259\n", + " 1259\n", + " Low\n", + " Low\n", + " 0.169369\n", + " 1.266205\n", + " 0.583186\n", + " 46.953360\n", + " 333.8133\n", + " 0.130586\n", + " 0.118583\n", " ...\n", - " -0.055092\n", - " 334.0576\n", - " 15.55901\n", - " 271.0670\n", - " 1.269063\n", - " 0.599918\n", - " -0.075514\n", - " 324.3901\n", - " 25.65784\n", - " 270.9597\n", + " 0.760320\n", + " 346.6641\n", + " 283.53940\n", + " 168.4210\n", + " 0.353179\n", + " 0.598251\n", + " 0.735154\n", + " 347.6538\n", + " 290.61270\n", + " 126.2281\n", " \n", " \n", - " 2023\n", - " 2023\n", - " High\n", - " High\n", - " 1.096437\n", - " 1.129293\n", - " 0.084847\n", - " 60.217070\n", - " 87.791440\n", - " 13.38561\n", - " 0.709858\n", + " 1260\n", + " 1260\n", + " Low\n", + " Low\n", + " 0.177724\n", + " 1.278330\n", + " 0.577999\n", + " 46.035750\n", + " 333.2926\n", + " 359.709700\n", + " 0.118528\n", " ...\n", - " -0.051225\n", - " 333.9445\n", - " 15.22374\n", - " 270.9147\n", - " 1.278922\n", - " 0.592047\n", - " -0.071804\n", - " 324.3320\n", - " 25.47795\n", - " 270.6435\n", + " 0.760351\n", + " 346.6641\n", + " 283.53940\n", + " 168.4210\n", + " 0.353123\n", + " 0.598169\n", + " 0.735184\n", + " 347.6538\n", + " 290.61270\n", + " 126.2281\n", " \n", " \n", - " 2024\n", - " 2024\n", - " High\n", - " High\n", - " 1.106890\n", - " 1.123694\n", - " 0.084149\n", - " 60.877950\n", - " 87.967790\n", - " 13.39271\n", - " 0.720758\n", + " 1261\n", + " 1261\n", + " Low\n", + " Low\n", + " 0.186001\n", + " 1.290231\n", + " 0.573633\n", + " 45.106170\n", + " 332.8138\n", + " 359.286200\n", + " 0.137214\n", " ...\n", - " -0.046066\n", - " 333.7580\n", - " 14.87231\n", - " 270.0383\n", - " 1.293765\n", - " 0.578599\n", - " -0.066561\n", - " 324.2077\n", - " 25.11721\n", - " 269.5140\n", + " 0.724006\n", + " 340.3340\n", + " 286.02930\n", + " 195.8516\n", + " 0.403420\n", + " 0.560423\n", + " 0.685445\n", + " 344.4067\n", + " 296.86830\n", + " 140.0699\n", " \n", " \n", "\n", - "

2025 rows × 339 columns

\n", + "

1262 rows × 339 columns

\n", "" ], "text/plain": [ " Unnamed: 0 LeftHandTrackingAccuracy RightHandTrackingAccuracy \\\n", - "0 0 High High \n", - "1 1 High High \n", - "2 2 High High \n", - "3 3 High High \n", - "4 4 High High \n", + "0 0 Low High \n", + "1 1 Low High \n", + "2 2 Low High \n", + "3 3 Low High \n", + "4 4 Low High \n", "... ... ... ... \n", - "2020 2020 High High \n", - "2021 2021 High High \n", - "2022 2022 High High \n", - "2023 2023 High High \n", - "2024 2024 High High \n", + "1257 1257 Low Low \n", + "1258 1258 Low Low \n", + "1259 1259 Low Low \n", + "1260 1260 Low Low \n", + "1261 1261 Low Low \n", "\n", " CenterEyeAnchor_pos_X CenterEyeAnchor_pos_Y CenterEyeAnchor_pos_Z \\\n", - "0 -0.089895 1.755665 0.234344 \n", - "1 -0.089738 1.755732 0.234542 \n", - "2 -0.089347 1.755780 0.234738 \n", - "3 -0.088938 1.755686 0.234353 \n", - "4 -0.088715 1.755643 0.234471 \n", + "0 0.681254 1.611774 0.702683 \n", + "1 0.681238 1.611911 0.702356 \n", + "2 0.681438 1.611861 0.702440 \n", + "3 0.681680 1.611776 0.702397 \n", + "4 0.681469 1.611685 0.702336 \n", "... ... ... ... \n", - "2020 1.067835 1.149886 0.087708 \n", - "2021 1.076106 1.142307 0.086917 \n", - "2022 1.085397 1.135880 0.086078 \n", - "2023 1.096437 1.129293 0.084847 \n", - "2024 1.106890 1.123694 0.084149 \n", + "1257 0.153791 1.242905 0.591736 \n", + "1258 0.161396 1.254340 0.587556 \n", + "1259 0.169369 1.266205 0.583186 \n", + "1260 0.177724 1.278330 0.577999 \n", + "1261 0.186001 1.290231 0.573633 \n", "\n", " CenterEyeAnchor_euler_X CenterEyeAnchor_euler_Y \\\n", - "0 9.332605 5.018498 \n", - "1 9.338191 5.061474 \n", - "2 9.343684 5.115413 \n", - "3 9.335765 5.173394 \n", - "4 9.326243 5.247888 \n", + "0 7.490442 348.7060 \n", + "1 7.481093 348.6785 \n", + "2 7.484574 348.6573 \n", + "3 7.490453 348.6290 \n", + "4 7.495254 348.6104 \n", "... ... ... \n", - "2020 57.752440 88.466320 \n", - "2021 58.376460 87.832810 \n", - "2022 59.298770 87.606250 \n", - "2023 60.217070 87.791440 \n", - "2024 60.877950 87.967790 \n", + "1257 48.695290 335.0239 \n", + "1258 47.867380 334.4086 \n", + "1259 46.953360 333.8133 \n", + "1260 46.035750 333.2926 \n", + "1261 45.106170 332.8138 \n", "\n", " CenterEyeAnchor_euler_Z left_OVRHandPrefab_pos_X ... \\\n", - "0 358.49900 -0.316633 ... \n", - "1 358.47840 -0.300754 ... \n", - "2 358.46080 -0.300725 ... \n", - "3 358.46820 -0.300701 ... \n", - "4 358.46250 -0.300564 ... \n", + "0 344.879200 0.679794 ... \n", + "1 344.882700 0.679784 ... \n", + "2 344.879200 0.680203 ... \n", + "3 344.880700 0.680205 ... \n", + "4 344.883500 0.680203 ... \n", "... ... ... ... \n", - "2020 13.97530 0.684950 ... \n", - "2021 13.81248 0.696102 ... \n", - "2022 13.54412 0.702457 ... \n", - "2023 13.38561 0.709858 ... \n", - "2024 13.39271 0.720758 ... \n", + "1257 0.940457 0.062155 ... \n", + "1258 0.544523 0.119903 ... \n", + "1259 0.130586 0.118583 ... \n", + "1260 359.709700 0.118528 ... \n", + "1261 359.286200 0.137214 ... \n", "\n", " right_Hand_RingTip_pos_Z right_Hand_RingTip_euler_X \\\n", - "0 0.446973 307.9409 \n", - "1 0.440061 308.1140 \n", - "2 0.439948 308.2395 \n", - "3 0.439995 308.3445 \n", - "4 0.439725 308.5142 \n", + "0 1.086929 302.7715 \n", + "1 1.092474 302.6280 \n", + "2 1.095202 302.4141 \n", + "3 1.097335 302.1731 \n", + "4 1.099373 301.9409 \n", "... ... ... \n", - "2020 -0.064587 334.3178 \n", - "2021 -0.058271 334.1573 \n", - "2022 -0.055092 334.0576 \n", - "2023 -0.051225 333.9445 \n", - "2024 -0.046066 333.7580 \n", + "1257 0.792824 349.6221 \n", + "1258 0.760241 346.6641 \n", + "1259 0.760320 346.6641 \n", + "1260 0.760351 346.6641 \n", + "1261 0.724006 340.3340 \n", "\n", " right_Hand_RingTip_euler_Y right_Hand_RingTip_euler_Z \\\n", - "0 320.01970 195.1051 \n", - "1 320.34520 195.5152 \n", - "2 320.51180 195.7206 \n", - "3 320.66750 195.9804 \n", - "4 320.82160 196.0655 \n", + "0 36.99136 137.2513 \n", + "1 37.42541 141.2726 \n", + "2 37.24395 144.9683 \n", + "3 36.79346 148.4191 \n", + "4 36.35692 151.8196 \n", "... ... ... \n", - "2020 16.43365 271.1187 \n", - "2021 15.85253 271.2372 \n", - "2022 15.55901 271.0670 \n", - "2023 15.22374 270.9147 \n", - "2024 14.87231 270.0383 \n", + "1257 280.89230 141.6955 \n", + "1258 283.53940 168.4210 \n", + "1259 283.53940 168.4210 \n", + "1260 283.53940 168.4210 \n", + "1261 286.02930 195.8516 \n", "\n", " right_Hand_PinkyTip_pos_X right_Hand_PinkyTip_pos_Y \\\n", - "0 0.016631 1.106319 \n", - "1 0.007256 1.145476 \n", - "2 0.007426 1.144992 \n", - "3 0.007635 1.144509 \n", - "4 0.007918 1.144068 \n", + "0 0.713257 0.870861 \n", + "1 0.714441 0.864941 \n", + "2 0.714923 0.863987 \n", + "3 0.715369 0.863188 \n", + "4 0.715776 0.862477 \n", "... ... ... \n", - "2020 1.245457 0.619399 \n", - "2021 1.260631 0.606978 \n", - "2022 1.269063 0.599918 \n", - "2023 1.278922 0.592047 \n", - "2024 1.293765 0.578599 \n", + "1257 0.292764 0.635226 \n", + "1258 0.352627 0.598427 \n", + "1259 0.353179 0.598251 \n", + "1260 0.353123 0.598169 \n", + "1261 0.403420 0.560423 \n", "\n", " right_Hand_PinkyTip_pos_Z right_Hand_PinkyTip_euler_X \\\n", - "0 0.430588 310.8786 \n", - "1 0.423464 311.1056 \n", - "2 0.423223 311.2307 \n", - "3 0.423137 311.3370 \n", - "4 0.422891 311.3891 \n", + "0 1.073421 316.0526 \n", + "1 1.075697 315.9267 \n", + "2 1.076074 315.5687 \n", + "3 1.076337 315.0991 \n", + "4 1.076571 314.6485 \n", "... ... ... \n", - "2020 -0.084547 324.5029 \n", - "2021 -0.078567 324.4428 \n", - "2022 -0.075514 324.3901 \n", - "2023 -0.071804 324.3320 \n", - "2024 -0.066561 324.2077 \n", + "1257 0.777857 348.3104 \n", + "1258 0.735074 347.6538 \n", + "1259 0.735154 347.6538 \n", + "1260 0.735184 347.6538 \n", + "1261 0.685445 344.4067 \n", "\n", " right_Hand_PinkyTip_euler_Y right_Hand_PinkyTip_euler_Z \n", - "0 343.57170 185.1874 \n", - "1 343.74560 185.6717 \n", - "2 343.79710 185.9125 \n", - "3 343.84570 186.1985 \n", - "4 343.80610 186.5528 \n", + "0 40.26445 156.0836 \n", + "1 40.45845 156.8290 \n", + "2 40.50327 157.4256 \n", + "3 40.50356 157.8922 \n", + "4 40.52340 158.2699 \n", "... ... ... \n", - "2020 26.11914 271.3724 \n", - "2021 25.80618 271.3020 \n", - "2022 25.65784 270.9597 \n", - "2023 25.47795 270.6435 \n", - "2024 25.11721 269.5140 \n", + "1257 289.75490 125.4430 \n", + "1258 290.61270 126.2281 \n", + "1259 290.61270 126.2281 \n", + "1260 290.61270 126.2281 \n", + "1261 296.86830 140.0699 \n", "\n", - "[2025 rows x 339 columns]" + "[1262 rows x 339 columns]" ] }, - "execution_count": 81, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1070,19 +1100,18 @@ "source": [ "def drop(entry) -> pd.DataFrame:\n", " droptable = ['participantID', 'FrameID', 'Scenario', 'HeightNormalization', 'ArmNormalization', 'Repetition', 'Session']\n", - " centry = entry.copy()\n", + " centry = pickle.loads(pickle.dumps(entry))\n", " return centry['data'].drop(droptable, axis=1)\n", "\n", - "test_entry2 = test_entry.copy()\n", - "test_entry2['data'] = drop(test_entry)\n", + "test_entry2 = pickle.loads(pickle.dumps(test_entry))\n", + "test_entry2['data'] = drop(test_entry2)\n", "test_entry2['data']" ] }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 95, - "id": "97c3ba71", + "execution_count": 16, + "id": "4f3ff073", "metadata": { "tags": [] }, @@ -1136,121 +1165,121 @@ " 0\n", " 0\n", " 0.0\n", - " 0.0\n", - " -0.089895\n", - " 1.755665\n", - " 0.234344\n", - " 9.332605\n", - " 5.018498\n", - " 358.49900\n", - " -0.316633\n", + " 1.0\n", + " 0.681254\n", + " 1.611774\n", + " 0.702683\n", + " 7.490442\n", + " 348.7060\n", + " 344.879200\n", + " 0.679794\n", " ...\n", - 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2025 rows × 339 columns

\n", + "

1262 rows × 339 columns

\n", "" ], "text/plain": [ " Unnamed: 0 LeftHandTrackingAccuracy RightHandTrackingAccuracy \\\n", - "0 0 0.0 0.0 \n", - "1 1 0.0 0.0 \n", - "2 2 0.0 0.0 \n", - "3 3 0.0 0.0 \n", - "4 4 0.0 0.0 \n", + "0 0 0.0 1.0 \n", + "1 1 0.0 1.0 \n", + "2 2 0.0 1.0 \n", + "3 3 0.0 1.0 \n", + "4 4 0.0 1.0 \n", "... ... ... ... \n", - "2020 2020 0.0 0.0 \n", - "2021 2021 0.0 0.0 \n", - "2022 2022 0.0 0.0 \n", - "2023 2023 0.0 0.0 \n", - "2024 2024 0.0 0.0 \n", + "1257 1257 0.0 0.0 \n", + "1258 1258 0.0 0.0 \n", + "1259 1259 0.0 0.0 \n", + "1260 1260 0.0 0.0 \n", + "1261 1261 0.0 0.0 \n", "\n", " CenterEyeAnchor_pos_X CenterEyeAnchor_pos_Y CenterEyeAnchor_pos_Z \\\n", - "0 -0.089895 1.755665 0.234344 \n", - "1 -0.089738 1.755732 0.234542 \n", - "2 -0.089347 1.755780 0.234738 \n", - "3 -0.088938 1.755686 0.234353 \n", - "4 -0.088715 1.755643 0.234471 \n", + "0 0.681254 1.611774 0.702683 \n", + "1 0.681238 1.611911 0.702356 \n", + "2 0.681438 1.611861 0.702440 \n", + "3 0.681680 1.611776 0.702397 \n", + "4 0.681469 1.611685 0.702336 \n", "... ... ... ... \n", - 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"0 0.016631 1.106319 \n", - "1 0.007256 1.145476 \n", - "2 0.007426 1.144992 \n", - "3 0.007635 1.144509 \n", - "4 0.007918 1.144068 \n", + "0 0.713257 0.870861 \n", + "1 0.714441 0.864941 \n", + "2 0.714923 0.863987 \n", + "3 0.715369 0.863188 \n", + "4 0.715776 0.862477 \n", "... ... ... \n", - "2020 1.245457 0.619399 \n", - "2021 1.260631 0.606978 \n", - "2022 1.269063 0.599918 \n", - "2023 1.278922 0.592047 \n", - "2024 1.293765 0.578599 \n", + "1257 0.292764 0.635226 \n", + "1258 0.352627 0.598427 \n", + "1259 0.353179 0.598251 \n", + "1260 0.353123 0.598169 \n", + "1261 0.403420 0.560423 \n", "\n", " right_Hand_PinkyTip_pos_Z right_Hand_PinkyTip_euler_X \\\n", - "0 0.430588 310.8786 \n", - "1 0.423464 311.1056 \n", - "2 0.423223 311.2307 \n", - "3 0.423137 311.3370 \n", - "4 0.422891 311.3891 \n", + "0 1.073421 316.0526 \n", + "1 1.075697 315.9267 \n", + "2 1.076074 315.5687 \n", + "3 1.076337 315.0991 \n", + "4 1.076571 314.6485 \n", "... ... ... \n", - "2020 -0.084547 324.5029 \n", - 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"execution_count": 95, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def floatize(entry):\n", - " centry = entry.copy()\n", + " centry = pickle.loads(pickle.dumps(entry))\n", " centry['data']['LeftHandTrackingAccuracy'] = (entry['data']['LeftHandTrackingAccuracy'] == 'High') * 1.0\n", " centry['data']['RightHandTrackingAccuracy'] = (entry['data']['RightHandTrackingAccuracy'] == 'High') * 1.0\n", " return centry['data']\n", "\n", - "test_entry3 = test_entry2.copy()\n", - "test_entry3['data'] = floatize(test_entry2)\n", + "test_entry3 = pickle.loads(pickle.dumps(test_entry2))\n", + "test_entry3['data'] = floatize(test_entry3)\n", "test_entry3['data']" ] }, { "cell_type": "code", - "execution_count": 96, - "id": "7ab1aa62", -======= - "execution_count": 11, - "id": "855aa409", ->>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 + "execution_count": 17, + "id": "2249d728", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - 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"1261 NaN NaN \n", + "\n", + " right_Hand_RingTip_euler_Y right_Hand_RingTip_euler_Z \\\n", + "0 36.99136 137.2513 \n", + "1 37.42541 141.2726 \n", + "2 37.24395 144.9683 \n", + "3 36.79346 148.4191 \n", + "4 36.35692 151.8196 \n", + "... ... ... \n", + "1257 NaN NaN \n", + "1258 NaN NaN \n", + "1259 NaN NaN \n", + "1260 NaN NaN \n", + "1261 NaN NaN \n", + "\n", + " right_Hand_PinkyTip_pos_X right_Hand_PinkyTip_pos_Y \\\n", + "0 0.713257 0.870861 \n", + "1 0.714441 0.864941 \n", + "2 0.714923 0.863987 \n", + "3 0.715369 0.863188 \n", + "4 0.715776 0.862477 \n", + "... ... ... \n", + "1257 NaN NaN \n", + "1258 NaN NaN \n", + "1259 NaN NaN \n", + "1260 NaN NaN \n", + "1261 NaN NaN \n", + "\n", + " right_Hand_PinkyTip_pos_Z right_Hand_PinkyTip_euler_X \\\n", + "0 1.073421 316.0526 \n", + "1 1.075697 315.9267 \n", + "2 1.076074 315.5687 \n", + "3 1.076337 315.0991 \n", + "4 1.076571 314.6485 \n", + "... ... ... \n", + "1257 NaN NaN \n", + "1258 NaN NaN \n", + "1259 NaN NaN \n", + "1260 NaN NaN \n", + "1261 NaN NaN \n", + "\n", + " right_Hand_PinkyTip_euler_Y right_Hand_PinkyTip_euler_Z \n", + "0 40.26445 156.0836 \n", + "1 40.45845 156.8290 \n", + "2 40.50327 157.4256 \n", + "3 40.50356 157.8922 \n", + "4 40.52340 158.2699 \n", + "... ... ... \n", + "1257 NaN NaN \n", + "1258 NaN NaN \n", + "1259 NaN NaN \n", + "1260 NaN NaN \n", + "1261 NaN NaN \n", + "\n", + "[1262 rows x 339 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" }, { "data": { + "image/png": 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\n", "text/plain": [ -<<<<<<< HEAD - "" + "
" ] }, - "execution_count": 96, -======= - "768" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "right_Hand_ident='right_Hand'\n", + "left_Hand_ident='left_hand'\n", + "\n", + "def rem_low_acc(entry):\n", + " centry = pickle.loads(pickle.dumps(entry))\n", + " right_Hand_cols = [c for c in centry['data'] if right_Hand_ident in c]\n", + " left_Hand_cols = [c for c in centry['data'] if left_Hand_ident in c]\n", + " \n", + " centry['data'].loc[centry['data']['RightHandTrackingAccuracy'] == 0.0, right_Hand_cols] = np.nan\n", + " centry['data'].loc[centry['data']['LeftHandTrackingAccuracy'] == 0.0, left_Hand_cols] = np.nan\n", + " return centry['data']\n", + "\n", + "test_entry4 = pickle.loads(pickle.dumps(test_entry3))\n", + "test_entry4['data'] = rem_low_acc(test_entry4)\n", + "\n", + "plt.plot(test_entry4['data']['right_Hand_RingTip_pos_X'])\n", + "plt.plot(test_entry4['data']['right_Hand_RingTip_pos_Y'])\n", + "plt.plot(test_entry4['data']['right_Hand_RingTip_pos_Z'])\n", + "\n", + "test_entry4['data']" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "b7e0ffcf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" ] }, - "execution_count": 11, ->>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def interpol(entry):\n", + " centry = pickle.loads(pickle.dumps(entry))\n", + " return centry['data'].interpolate()\n", + "\n", + "test_entry5 = pickle.loads(pickle.dumps(test_entry4))\n", + "test_entry5['data'] = interpol(test_entry5)\n", + "\n", + "plt.plot(test_entry5['data']['right_Hand_RingTip_pos_X'])\n", + "plt.plot(test_entry5['data']['right_Hand_RingTip_pos_Y'])\n", + "plt.plot(test_entry5['data']['right_Hand_RingTip_pos_Z'])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "12125fe9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1580,7 +2115,6 @@ "\n", "def slicing(entry):\n", " stride = 150\n", - " print(entry['data'].to_numpy().shape)\n", " entry['data'] = pad_sequences(entry['data'].to_numpy(),\n", " maxlen=(int(entry['data'].shape[0]/stride)+1)*stride,\n", " dtype='float64',\n", @@ -1596,461 +2130,129 @@ " seed=177013\n", " )\n", "\n", - "test_entry4 = test_entry3.copy()\n", - "test_entry4['data'] = slicing(test_entry3)\n", - "test_entry4['data']" + "test_entry6 = pickle.loads(pickle.dumps(test_entry5))\n", + "test_entry6['data'] = slicing(test_entry6)\n", + "test_entry6['data']" ] }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 97, - "id": "8827bbab", -======= - "execution_count": 12, - "id": "e1d660ea", + "execution_count": 23, + "id": "d6a9be7c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'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", - "0 0 0 2 SortingBlocksScene \n", - "1 1 1 2 SortingBlocksScene \n", - "2 2 2 2 SortingBlocksScene \n", - "3 3 3 2 SortingBlocksScene \n", - "4 4 4 2 SortingBlocksScene \n", - "... ... ... ... ... \n", - "1734 1734 1734 2 SortingBlocksScene \n", - "1735 1735 1735 2 SortingBlocksScene \n", - "1736 1736 1736 2 SortingBlocksScene \n", - "1737 1737 1737 2 SortingBlocksScene \n", - "1738 1738 1738 2 SortingBlocksScene \n", - "\n", - " HeightNormalization ArmNormalization Repetition \\\n", - "0 False True 0 \n", - "1 False True 0 \n", - "2 False True 0 \n", - "3 False True 0 \n", - "4 False True 0 \n", - "... ... ... ... \n", - "1734 False True 0 \n", - "1735 False True 0 \n", - "1736 False True 0 \n", - "1737 False True 0 \n", - "1738 False True 0 \n", - "\n", - " LeftHandTrackingAccuracy CenterEyeAnchor_pos_X CenterEyeAnchor_pos_Y \\\n", - "0 High 0.092306 1.541967 \n", - "1 High 0.092339 1.542134 \n", - "2 High 0.092368 1.542324 \n", - "3 High 0.092528 1.542059 \n", - "4 High 0.092597 1.541883 \n", - "... ... ... ... \n", - "1734 Low -0.118714 1.209641 \n", - "1735 Low -0.120236 1.208805 \n", - "1736 Low -0.121484 1.208166 \n", - "1737 Low -0.122358 1.207728 \n", - "1738 Low -0.123330 1.207573 \n", - "\n", - " ... right_Hand_RingTip_euler_Y right_Hand_RingTip_euler_Z \\\n", - "0 ... 22.05669 133.1912 \n", - "1 ... 22.27071 132.8817 \n", - "2 ... 22.46026 132.6752 \n", - "3 ... 22.69390 132.5095 \n", - "4 ... 22.71980 132.6141 \n", - "... ... ... ... \n", - "1734 ... 276.90730 109.5109 \n", - "1735 ... 276.74050 110.6240 \n", - "1736 ... 276.70190 111.3780 \n", - "1737 ... 275.85200 112.9337 \n", - "1738 ... 275.01000 114.0286 \n", - "\n", - " right_Hand_PinkyTip_pos_X right_Hand_PinkyTip_pos_Y \\\n", - "0 0.153212 1.137668 \n", - "1 0.144130 1.161759 \n", - "2 0.144180 1.161877 \n", - "3 0.144442 1.161859 \n", - "4 0.144659 1.161498 \n", - "... ... ... \n", - "1734 0.079172 0.720395 \n", - "1735 0.076673 0.720138 \n", - "1736 0.075629 0.720296 \n", - "1737 0.076548 0.716786 \n", - "1738 0.078380 0.713724 \n", - "\n", - " right_Hand_PinkyTip_pos_Z right_Hand_PinkyTip_euler_X \\\n", - "0 0.254763 319.9573 \n", - "1 0.240258 320.2673 \n", - "2 0.240361 320.5427 \n", - "3 0.240253 320.9518 \n", - "4 0.239949 321.0735 \n", - "... ... ... \n", - "1734 1.235342 350.7823 \n", - "1735 1.245831 351.2207 \n", - "1736 1.252426 351.6184 \n", - "1737 1.262976 352.0619 \n", - "1738 1.269815 352.2750 \n", - "\n", - " right_Hand_PinkyTip_euler_Y right_Hand_PinkyTip_euler_Z Session \\\n", - "0 23.96579 143.5809 1 \n", - "1 23.99540 143.5675 1 \n", - "2 24.01744 143.6467 1 \n", - "3 23.98431 144.0452 1 \n", - "4 23.90498 144.4992 1 \n", - "... ... ... ... \n", - "1734 284.40790 113.3901 1 \n", - "1735 284.28250 114.6937 1 \n", - "1736 284.30020 115.6711 1 \n", - "1737 283.35190 117.7055 1 \n", - "1738 282.37350 119.1508 1 \n", - "\n", - " RightHandTrackingAccuracy \n", - "0 High \n", - "1 High \n", - "2 High \n", - "3 High \n", - "4 High \n", - "... ... \n", - "1734 Low \n", - "1735 Low \n", - "1736 Low \n", - "1737 Low \n", - "1738 Low \n", - "\n", - "[1739 rows x 346 columns]}\n" + "7\n", + "tf.Tensor(\n", + "[[[ 0. 0. 0. ... 316.0526 40.26445\n", + " 156.0836 ]\n", + " [ 0. 0. 0. ... 315.9267 40.45845\n", + " 156.829 ]\n", + " [ 0. 0. 0. ... 315.5687 40.50327\n", + " 157.4256 ]\n", + " ...\n", + " [ 0. 0. 0. ... 327.92606222 162.40435556\n", + " 268.09900667]\n", + " [ 0. 0. 0. ... 328.23225444 162.34076111\n", + " 267.87249333]\n", + " [ 0. 0. 0. ... 328.53844667 162.27716667\n", + " 267.64598 ]]], shape=(1, 300, 1350), dtype=float64)\n", + "tf.Tensor([1], shape=(1,), dtype=int32)\n", + "tf.Tensor(\n", + "[[[ 0. 0. 0. ... 352.5714 179.1548 217.5497]\n", + " [ 0. 0. 0. ... 352.1808 177.5313 219.296 ]\n", + " [ 0. 0. 0. ... 350.6774 174.4262 226.6657]\n", + " ...\n", + " [ 0. 0. 0. ... 344.1929 153.8235 240.5687]\n", + " [ 0. 0. 0. ... 344.1672 153.5105 238.4677]\n", + " [ 0. 0. 0. ... 343.5467 153.3172 234.248 ]]], shape=(1, 300, 1350), dtype=float64)\n", + "tf.Tensor([1], shape=(1,), dtype=int32)\n", + "tf.Tensor(\n", + "[[[ 0. 0. 0. ... 328.84463889 162.21357222\n", + " 267.41946667]\n", + " [ 0. 0. 0. ... 329.15083111 162.14997778\n", + " 267.19295333]\n", + " [ 0. 0. 0. ... 329.45702333 162.08638333\n", + " 266.96644 ]\n", + " ...\n", + " [ 0. 0. 0. ... 332.49044885 141.66310611\n", + " 290.62965115]\n", + " [ 0. 0. 0. ... 332.54811221 141.55510153\n", + " 291.25838779]\n", + " [ 0. 0. 0. ... 332.60577557 141.44709695\n", + " 291.88712443]]], shape=(1, 300, 1350), dtype=float64)\n", + "tf.Tensor([1], shape=(1,), dtype=int32)\n", + "tf.Tensor(\n", + "[[[ 0. 0. 0. ... 343.0005 153.3162\n", + " 231.6987 ]\n", + " [ 0. 0. 0. ... 342.5247 153.2595\n", + " 229.2278 ]\n", + " [ 0. 0. 0. ... 341.5173 153.4126\n", + " 229.2072 ]\n", + " ...\n", + " [ 0. 0. 0. ... 79.14743952 127.82502188\n", + " 284.14814688]\n", + " [ 0. 0. 0. ... 82.5359335 127.607275\n", + " 283.617075 ]\n", + " [ 0. 0. 0. ... 85.92442748 127.38952812\n", + " 283.08600312]]], shape=(1, 300, 1350), dtype=float64)\n", + "tf.Tensor([1], shape=(1,), dtype=int32)\n", + "tf.Tensor(\n", + "[[[ 0. 0. 0. ... 332.66343893 141.33909237\n", + " 292.51586107]\n", + " [ 0. 0. 0. ... 332.72110229 141.23108779\n", + " 293.14459771]\n", + " [ 0. 0. 0. ... 332.77876565 141.12308321\n", + " 293.77333435]\n", + " ...\n", + " [ 0. 0. 0. ... 345.114 96.71846\n", + " 272.46 ]\n", + " [ 0. 0. 0. ... 346.0049 97.19048\n", + " 274.314 ]\n", + " [ 0. 0. 0. ... 346.6387 97.76556\n", + " 275.7643 ]]], shape=(1, 300, 1350), dtype=float64)\n", + "tf.Tensor([1], shape=(1,), dtype=int32)\n", + "tf.Tensor(\n", + "[[[ 0. 0. 0. ... 89.31292146 127.17178125\n", + " 282.55493125]\n", + " [ 0. 0. 0. ... 92.70141544 126.95403438\n", + " 282.02385937]\n", + " [ 0. 0. 0. ... 96.08990942 126.7362875\n", + " 281.4927875 ]\n", + " ...\n", + " [ 0. 0. 0. ... 315.7431 40.94952\n", + " 261.4432 ]\n", + " [ 0. 0. 0. ... 315.6548 39.27138\n", + " 264.3384 ]\n", + " [ 0. 0. 0. ... 315.6677 37.37699\n", + " 267.8434 ]]], shape=(1, 300, 1350), dtype=float64)\n", + "tf.Tensor([1], shape=(1,), dtype=int32)\n", + "tf.Tensor(\n", + "[[[ 0. 0. 0. ... 347.3555 98.27622\n", + " 277.2787 ]\n", + " [ 0. 0. 0. ... 347.8575 99.32357\n", + " 277.5592 ]\n", + " [ 0. 0. 0. ... 348.1747 100.1475\n", + " 277.55 ]\n", + " ...\n", + " [ 0. 0. 0. ... 325.04633636 321.95206364\n", + " 115.3589 ]\n", + " [ 0. 0. 0. ... 324.85737273 319.39122727\n", + " 115.5281 ]\n", + " [ 0. 0. 0. ... 324.66840909 316.83039091\n", + " 115.6973 ]]], shape=(1, 300, 1350), dtype=float64)\n", + "tf.Tensor([1], shape=(1,), dtype=int32)\n" ] } ], "source": [ - "print (dic_data[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "6284add1", ->>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tf.Tensor(\n", - "[[[ 0. 0. 0. ... 310.8786 343.5717 185.1874]\n", - " [ 0. 0. 0. ... 311.1056 343.7456 185.6717]\n", - " [ 0. 0. 0. ... 311.2307 343.7971 185.9125]\n", - " ...\n", - " [ 0. 0. 0. ... 334.9102 105.7513 287.5223]\n", - " [ 0. 0. 0. ... 334.6709 104.5253 287.9581]\n", - " [ 0. 0. 0. ... 334.5766 103.5059 288.4085]]], shape=(1, 300, 2100), dtype=float64)\n", - "tf.Tensor([7], shape=(1,), dtype=int32)\n", - "tf.Tensor(\n", - "[[[ 0. 0. 0. ... 337.3954 115.2253 239.2146 ]\n", - " [ 0. 0. 0. ... 337.7683 115.3956 240.4796 ]\n", - " [ 0. 0. 0. ... 338.0132 115.4679 241.2587 ]\n", - " ...\n", - " [ 0. 0. 0. ... 332.9107 41.93251 124.3949 ]\n", - " [ 0. 0. 0. ... 333.1059 40.51485 124.8919 ]\n", - " [ 0. 0. 0. ... 332.9174 39.48595 125.2935 ]]], shape=(1, 300, 2100), dtype=float64)\n", - "tf.Tensor([7], shape=(1,), dtype=int32)\n", - "tf.Tensor(\n", - "[[[ 0. 0. 0. ... 334.441 102.5691 288.6859]\n", - " [ 0. 0. 0. ... 334.4541 101.1844 290.0429]\n", - " [ 0. 0. 0. ... 334.5445 100.4508 291.0764]\n", - " ...\n", - " [ 0. 0. 0. ... 342.0367 322.5176 287.4604]\n", - " [ 0. 0. 0. ... 341.5087 322.9041 288.0399]\n", - " [ 0. 0. 0. ... 341.0814 323.1584 288.53 ]]], shape=(1, 300, 2100), dtype=float64)\n", - "tf.Tensor([7], shape=(1,), dtype=int32)\n", - "tf.Tensor(\n", - "[[[ 0. 0. 0. ... 332.7831 38.47573 125.6316 ]\n", - " [ 0. 0. 0. ... 332.4827 36.4628 126.487 ]\n", - " [ 0. 0. 0. ... 332.2903 35.13992 127.0712 ]\n", - " ...\n", - " [ 0. 0. 0. ... 358.9955 91.98264 223.499 ]\n", - " [ 0. 0. 0. ... 358.6208 91.1073 225.3601 ]\n", - " [ 0. 0. 0. ... 358.354 90.26964 227.1508 ]]], shape=(1, 300, 2100), dtype=float64)\n", - "tf.Tensor([7], shape=(1,), dtype=int32)\n", - "tf.Tensor(\n", - "[[[ 0. 0. 0. ... 340.7549 323.3292 289.6002 ]\n", - " [ 0. 0. 0. ... 340.5391 323.4655 290.4398 ]\n", - " [ 0. 0. 0. ... 340.3628 323.5816 291.6447 ]\n", - " ...\n", - " [ 0. 0. 0. ... 334.6424 149.6268 96.6635 ]\n", - " [ 0. 0. 0. ... 335.1271 151.0876 96.39161]\n", - " [ 0. 0. 0. ... 335.5759 152.4971 96.23958]]], shape=(1, 300, 2100), dtype=float64)\n", - "tf.Tensor([7], shape=(1,), dtype=int32)\n", - "tf.Tensor(\n", - "[[[ 0. 0. 0. ... 358.0558 89.08697 228.282 ]\n", - " [ 0. 0. 0. ... 357.9095 88.468 228.3914 ]\n", - " [ 0. 0. 0. ... 357.7622 87.76212 228.7186 ]\n", - " ...\n", - " [ 0. 0. 0. ... 350.4678 191.8058 293.6591 ]\n", - " [ 0. 0. 0. ... 349.4155 191.758 290.0341 ]\n", - " [ 0. 0. 0. ... 348.4454 191.7047 286.47 ]]], shape=(1, 300, 2100), dtype=float64)\n", - "tf.Tensor([7], shape=(1,), dtype=int32)\n", - "tf.Tensor(\n", - "[[[ 0. 0. 0. ... 336.1516 154.5288 95.77281 ]\n", - " [ 0. 0. 0. ... 336.4283 155.5994 95.43106 ]\n", - " [ 0. 0. 0. ... 336.7111 156.6993 95.16699 ]\n", - " ...\n", - " [ 0. 0. 0. ... 1.877451 189.7006 176.2247 ]\n", - " [ 0. 0. 0. ... 2.298272 189.6146 174.1894 ]\n", - " [ 0. 0. 0. ... 2.357256 189.587 173.9407 ]]], shape=(1, 300, 2100), dtype=float64)\n", - "tf.Tensor([7], shape=(1,), dtype=int32)\n", - "tf.Tensor(\n", - "[[[ 0. 0. 0. ... 347.4656 193.364 277.8619 ]\n", - " [ 0. 0. 0. ... 347.1607 194.9414 272.1756 ]\n", - " [ 0. 0. 0. ... 346.816 196.2879 266.7031 ]\n", - " ...\n", - " [ 0. 0. 0. ... 328.2634 195.3502 53.96029]\n", - " [ 0. 0. 0. ... 328.1786 200.2938 56.4752 ]\n", - " [ 0. 0. 0. ... 328.5285 204.5934 59.88613]]], shape=(1, 300, 2100), dtype=float64)\n", - "tf.Tensor([7], shape=(1,), dtype=int32)\n", - "tf.Tensor(\n", - "[[[ 0. 0. 0. ... 2.574736 189.5766 172.9151 ]\n", - " [ 0. 0. 0. ... 2.947406 189.6876 171.4222 ]\n", - " [ 0. 0. 0. ... 3.538999 190.0329 169.4787 ]\n", - " ...\n", - " [ 0. 0. 0. ... 339.8397 195.9552 144.3933 ]\n", - " [ 0. 0. 0. ... 339.8964 192.4409 148.1278 ]\n", - " [ 0. 0. 0. ... 340.1847 190.5611 150.9379 ]]], shape=(1, 300, 2100), dtype=float64)\n", - "tf.Tensor([7], shape=(1,), dtype=int32)\n", - "tf.Tensor(\n", - "[[[ 0. 0. 0. ... 328.807 209.356 64.13982]\n", - " [ 0. 0. 0. ... 329.1744 219.1686 82.03358]\n", - " [ 0. 0. 0. ... 328.9293 224.907 95.42315]\n", - " ...\n", - " [ 0. 0. 0. ... 311.8901 182.3479 207.4501 ]\n", - " [ 0. 0. 0. ... 296.0581 43.59404 291.2405 ]\n", - " [ 0. 0. 0. ... 296.0581 43.59404 291.2405 ]]], shape=(1, 300, 2100), dtype=float64)\n", - "tf.Tensor([7], shape=(1,), dtype=int32)\n", - "tf.Tensor(\n", - "[[[ 0. 0. 0. ... 340.4522 188.5802 153.4675]\n", - " [ 0. 0. 0. ... 342.0439 186.2218 158.3235]\n", - " [ 0. 0. 0. ... 343.4704 184.4892 161.9352]\n", - " ...\n", - " [ 0. 0. 0. ... 336.0501 100.8427 308.4322]\n", - " [ 0. 0. 0. ... 336.5646 102.0053 307.9174]\n", - " [ 0. 0. 0. ... 336.9454 102.9008 307.495 ]]], shape=(1, 300, 2100), dtype=float64)\n", - "tf.Tensor([7], shape=(1,), dtype=int32)\n", - "tf.Tensor(\n", - "[[[ 0. 0. 0. ... 296.0581 43.59404 291.2405 ]\n", - " [ 0. 0. 0. ... 318.9684 302.7743 264.226 ]\n", - " [ 0. 0. 0. ... 318.9684 302.7743 264.226 ]\n", - " ...\n", - " [ 0. 0. 0. ... 321.3094 67.3396 295.069 ]\n", - " [ 0. 0. 0. ... 321.406 66.64935 293.5298 ]\n", - " [ 0. 0. 0. ... 321.696 65.39925 291.8149 ]]], shape=(1, 300, 2100), dtype=float64)\n", - "tf.Tensor([7], shape=(1,), dtype=int32)\n" - ] - } - ], - "source": [ - "for i,t in test_entry4['data']:\n", - " print(i)\n", - " print(t)" - ] - }, - { - "cell_type": "code", -<<<<<<< HEAD - "execution_count": 126, - "id": "bcec87a9", -======= - "execution_count": 5, - "id": "82167504", ->>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ -<<<<<<< HEAD - "/opt/iui-datarelease3-sose2021/P14_ScenarioSorting_HeightNormalizationTrue_ArmNormalizationTrue_Repetition2.csv\n" -======= - "/opt/iui-datarelease3-sose2021/P7_ScenarioSorting_HeightNormalizationFalse_ArmNormalizationFalse_Repetition0_Session1.csv\n" ->>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 - ] - }, - { - "data": { - "text/plain": [ - "336" - ] - }, -<<<<<<< HEAD - "execution_count": 126, -======= - "execution_count": 5, ->>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "index = 1\n", - "entry = fil_dic_data[index]['data']\n", - "print(fil_dic_data[index]['filename'])\n", - "col_of_interst = []\n", - "for col in entry:\n", - " if 'float' in str(entry[col].dtype):\n", - " col_of_interst.append(col)\n", - "len(col_of_interst)" - ] - }, - { - "cell_type": "code", -<<<<<<< HEAD - "execution_count": 98, - "id": "fb10decc", -======= - "execution_count": 6, - "id": "badc87d6", ->>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 - "metadata": {}, - "outputs": [], - "source": [ - "len_list = []\n", - "for i in dic_data:\n", - " len_list.append(len(i['data']))" - ] - }, - { - "cell_type": "code", -<<<<<<< HEAD - "execution_count": 110, - "id": "08ae9f52", -======= - "execution_count": 7, - "id": "97c17107", ->>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 768.000000\n", - "mean 2606.889323\n", - "std 1941.835990\n", - "min 407.000000\n", - "50% 2102.000000\n", - "90% 4427.900000\n", - "91% 4606.940000\n", - "92% 4788.760000\n", - "93% 5131.990000\n", - "94% 5815.100000\n", - "95% 6182.450000\n", - "96% 6400.920000\n", - "97% 7223.400000\n", - "98% 8273.100000\n", - "99% 10162.200000\n", - "max 21108.000000\n", - "dtype: float64" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len_series = pd.Series(len_list, dtype='int64')\n", - "len_series.describe(percentiles=[x*0.01 for x in range(90,100)])" - ] - }, - { - "cell_type": "code", -<<<<<<< HEAD - "execution_count": 111, - "id": "57fee43e", -======= - "execution_count": 8, - "id": "ab1295b4", ->>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "\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", -<<<<<<< HEAD - "execution_count": 12, - "id": "69bd85a5", - "metadata": {}, - "outputs": [], - "source": [ - "def pplot(dd):\n", - " x = dd.shape[0]\n", - " fix = int(x/3)+1\n", - " fiy = 3\n", - " fig, axs = plt.subplots(fix, fiy, figsize=(3*fiy, 3*fix))\n", - " \n", - " for i in range(x):\n", - " axs[int(i/3)][i%3].plot(dd[i])\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "522518bc", -======= - "execution_count": 9, - "id": "b530d28c", ->>>>>>> e79bab7e2c685e07ae684a42fa4c84dc8b65a5e7 - "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\u001b[0m in \u001b[0;36m\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": [ - "pplot(x['data'][2:12])" + "print(len(test_entry6['data']))\n", + "for d,l in test_entry6['data']:\n", + " print(d)\n", + " print(l)" ] } ],