added owncloud data request notebook

reading full "SIGNAL" table for 4 columns might kill the kernel. Maybe use partial requests in pd.read_hdf() with start and stop

owncloud.ipynb

@@ -0,0 +1,137 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": null,
+   "id": "4f42846c-27c3-4394-a40a-e22d73c2902e",
+   "metadata": {},
+   "outputs": [],
+   "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-0003-v_0_0_2.h5py\"\n",
+    "oc = owncloud.Client.from_public_link(url, folder_password=password)\n",
+    "\n",
+    "\n",
+    "oc.get_file(file, \"tmp.h5\")\n",
+    "\n",
+    "end = time.time()\n",
+    "print(end - start)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3714dec2-85d0-4f76-af46-ea45ebec2fa3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start = time.time()\n",
+    "df_performance =  pd.read_hdf(\"tmp.h5\", \"PERFORMANCE\")\n",
+    "end = time.time()\n",
+    "print(end - start)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ae47e52-ad86-4f8d-b929-0080dc99f646",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start = time.time()\n",
+    "df_4_col =  pd.read_hdf(\"tmp.h5\", \"SIGNALS\", mode=\"r\", columns=[\"STUDY\",\"LEVEL\", \"PHASE\", 'RAW_ECG_I'])\n",
+    "end = time.time()\n",
+    "print(end - start)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c139f3a-ede8-4530-957d-d1bb939f6cb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_4_col.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a68d58ea-65f2-46c4-a2b2-8c3447c715d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_4_col.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95aa4523-3784-4ab6-bf92-0227ce60e863",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_4_col.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "defbcaf4-ad1b-453f-9b48-ab0ecfc4b5d5",
+   "metadata": {},
+   "outputs": [],
+   "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": "Python 3 (ipykernel)",
+   "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": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}