138 lines
2.9 KiB
Plaintext
138 lines
2.9 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": "aab6b326-a583-47ad-8bb7-723c2fddcc63",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"%pip install pyocclient\n",
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"import yaml\n",
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"import owncloud\n",
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"import pandas as pd\n",
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"import time"
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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": null,
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"id": "4f42846c-27c3-4394-a40a-e22d73c2902e",
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"metadata": {},
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"outputs": [],
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"source": [
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"start = time.time()\n",
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"\n",
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"with open(\"login.yaml\") as f:\n",
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" cfg = yaml.safe_load(f)\n",
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"url, password = cfg[0][\"url\"], cfg[1][\"password\"]\n",
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"file = \"adabase-public-0003-v_0_0_2.h5py\"\n",
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"oc = owncloud.Client.from_public_link(url, folder_password=password)\n",
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"\n",
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"\n",
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"oc.get_file(file, \"tmp.h5\")\n",
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"\n",
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"end = time.time()\n",
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"print(end - start)\n"
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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": null,
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"id": "3714dec2-85d0-4f76-af46-ea45ebec2fa3",
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"metadata": {},
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"outputs": [],
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"source": [
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"start = time.time()\n",
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"df_performance = pd.read_hdf(\"tmp.h5\", \"PERFORMANCE\")\n",
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"end = time.time()\n",
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"print(end - start)"
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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": null,
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"id": "6ae47e52-ad86-4f8d-b929-0080dc99f646",
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"metadata": {},
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"outputs": [],
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"source": [
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"start = time.time()\n",
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"df_4_col = pd.read_hdf(\"tmp.h5\", \"SIGNALS\", mode=\"r\", columns=[\"STUDY\",\"LEVEL\", \"PHASE\", ''])\n",
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"end = time.time()\n",
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"print(end - start)"
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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": null,
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"id": "7c139f3a-ede8-4530-957d-d1bb939f6cb5",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_4_col.head()"
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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": null,
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"id": "a68d58ea-65f2-46c4-a2b2-8c3447c715d7",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_4_col.shape"
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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": null,
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"id": "95aa4523-3784-4ab6-bf92-0227ce60e863",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_4_col.info()"
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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": null,
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"id": "defbcaf4-ad1b-453f-9b48-ab0ecfc4b5d5",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_4_col.isna().sum()"
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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": null,
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"id": "72313895-c478-44a5-9108-00b0bec01bb8",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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