{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "aab6b326-a583-47ad-8bb7-723c2fddcc63", "metadata": { "scrolled": true }, "outputs": [], "source": [ "# %pip install pyocclient\n", "import yaml\n", "import owncloud\n", "import pandas as pd\n", "import time" ] }, { "cell_type": "code", "execution_count": 2, "id": "4f42846c-27c3-4394-a40a-e22d73c2902e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "143.946026802063\n" ] } ], "source": [ "start = time.time()\n", "\n", "with open(\"../login.yaml\") as f:\n", " cfg = yaml.safe_load(f)\n", "url, password = cfg[0][\"url\"], cfg[1][\"password\"]\n", "file = \"adabase-public-0022-v_0_0_2.h5py\"\n", "oc = owncloud.Client.from_public_link(url, folder_password=password)\n", "\n", "\n", "oc.get_file(file, \"tmp22.h5\")\n", "\n", "end = time.time()\n", "print(end - start)\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "3714dec2-85d0-4f76-af46-ea45ebec2fa3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5121121406555176\n" ] } ], "source": [ "start = time.time()\n", "df_performance = pd.read_hdf(\"tmp22.h5\", \"PERFORMANCE\")\n", "end = time.time()\n", "print(end - start)" ] }, { "cell_type": "code", "execution_count": 4, "id": "f50e97d0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "22\n" ] } ], "source": [ "print(22)" ] }, { "cell_type": "code", "execution_count": 5, "id": "c131c816", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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STUDYPHASELEVELAUDITIVE F1AUDITIVE MEAN REACTION TIMEAUDITIVE PRECISIONAUDITIVE RECALLVISUAL F1VISUAL MEAN REACTION TIMEVISUAL PRECISIONVISUAL RECALLF1PRECISIONREACTION TIMERECALLSONGS RECALL
6n-backtest01NaNNaNNaNNaN1.0000000.4280681.0000001.000000NaNNaNNaNNaNNaN
7n-backtest02NaNNaNNaNNaN0.9285710.6268691.0000000.866667NaNNaNNaNNaNNaN
8n-backtest03NaNNaNNaNNaN0.6400000.8289120.7272730.571429NaNNaNNaNNaNNaN
9n-backtest041.0000001.3092861.0000001.0000001.0000000.9429161.0000001.000000NaNNaNNaNNaNNaN
10n-backtest050.7826091.3164840.8181820.7500000.8148151.1514050.9166670.733333NaNNaNNaNNaNNaN
11n-backtest060.3636361.7035830.5000000.2857140.4761901.5300540.7142860.357143NaNNaNNaNNaNNaN
3k-drivetest01NaNNaNNaNNaNNaNNaNNaNNaN1.0000001.0000000.4469141.000000NaN
4k-drivetest02NaNNaNNaNNaNNaNNaNNaNNaN0.9142860.9142860.7025710.9142860.454545
5k-drivetest03NaNNaNNaNNaNNaNNaNNaNNaN0.7863250.9387761.1757970.6764710.347826
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" ], "text/plain": [ " STUDY PHASE LEVEL AUDITIVE F1 AUDITIVE MEAN REACTION TIME \\\n", "6 n-back test 01 NaN NaN \n", "7 n-back test 02 NaN NaN \n", "8 n-back test 03 NaN NaN \n", "9 n-back test 04 1.000000 1.309286 \n", "10 n-back test 05 0.782609 1.316484 \n", "11 n-back test 06 0.363636 1.703583 \n", "3 k-drive test 01 NaN NaN \n", "4 k-drive test 02 NaN NaN \n", "5 k-drive test 03 NaN NaN \n", "\n", " AUDITIVE PRECISION AUDITIVE RECALL VISUAL F1 VISUAL MEAN REACTION TIME \\\n", "6 NaN NaN 1.000000 0.428068 \n", "7 NaN NaN 0.928571 0.626869 \n", "8 NaN NaN 0.640000 0.828912 \n", "9 1.000000 1.000000 1.000000 0.942916 \n", "10 0.818182 0.750000 0.814815 1.151405 \n", "11 0.500000 0.285714 0.476190 1.530054 \n", "3 NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN \n", "5 NaN NaN NaN NaN \n", "\n", " VISUAL PRECISION VISUAL RECALL F1 PRECISION REACTION TIME \\\n", "6 1.000000 1.000000 NaN NaN NaN \n", "7 1.000000 0.866667 NaN NaN NaN \n", "8 0.727273 0.571429 NaN NaN NaN \n", "9 1.000000 1.000000 NaN NaN NaN \n", "10 0.916667 0.733333 NaN NaN NaN \n", "11 0.714286 0.357143 NaN NaN NaN \n", "3 NaN NaN 1.000000 1.000000 0.446914 \n", "4 NaN NaN 0.914286 0.914286 0.702571 \n", "5 NaN NaN 0.786325 0.938776 1.175797 \n", "\n", " RECALL SONGS RECALL \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "10 NaN NaN \n", "11 NaN NaN \n", "3 1.000000 NaN \n", "4 0.914286 0.454545 \n", "5 0.676471 0.347826 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_performance" ] }, { "cell_type": "code", "execution_count": 6, "id": "6ae47e52-ad86-4f8d-b929-0080dc99f646", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.05357074737548828\n" ] } ], "source": [ "start = time.time()\n", "df_4_col = pd.read_hdf(\"tmp.h5\", \"SIGNALS\", mode=\"r\", columns=[\"STUDY\"], start=0, stop=1)\n", "end = time.time()\n", "print(end - start)" ] }, { "cell_type": "code", "execution_count": 7, "id": "7c139f3a-ede8-4530-957d-d1bb939f6cb5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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STUDY
0n/a
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" ], "text/plain": [ " STUDY\n", "0 n/a" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_4_col.head()" ] }, { "cell_type": "code", "execution_count": 8, "id": "a68d58ea-65f2-46c4-a2b2-8c3447c715d7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1, 1)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_4_col.shape" ] }, { "cell_type": "code", "execution_count": 9, "id": "95aa4523-3784-4ab6-bf92-0227ce60e863", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 1 entries, 0 to 0\n", "Data columns (total 1 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 STUDY 1 non-null object\n", "dtypes: object(1)\n", "memory usage: 16.0+ bytes\n" ] } ], "source": [ "df_4_col.info()" ] }, { "cell_type": "code", "execution_count": 10, "id": "defbcaf4-ad1b-453f-9b48-ab0ecfc4b5d5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "STUDY 0\n", "dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_4_col.isna().sum()" ] }, { "cell_type": "code", "execution_count": null, "id": "72313895-c478-44a5-9108-00b0bec01bb8", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "base", "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }