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diff --git a/001-Jupyter/002-JupyterExtensions/AppMode.ipynb b/001-Jupyter/002-JupyterExtensions/AppMode.ipynb
deleted file mode 100644
index a961601ccf9402974bf3edb7377e288a5082858d..0000000000000000000000000000000000000000
--- a/001-Jupyter/002-JupyterExtensions/AppMode.ipynb
+++ /dev/null
@@ -1,77 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "0be912cbed99416190ac97e3a44905e4",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "VBox(children=(Text(value='', disabled=True, layout=Layout(width='190px'), placeholder='0'), HBox(children=(Bu…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from __future__ import division\n",
- "import ipywidgets as ipw\n",
- "\n",
- "output = ipw.Text(placeholder=\"0\", layout=ipw.Layout(width=\"190px\"), disabled=True)\n",
- "\n",
- "def on_click(btn):\n",
- " if btn.description == \"=\":\n",
- " try:\n",
- " output.value = str(eval(output.value))\n",
- " except:\n",
- " output.value = \"ERROR\"\n",
- " elif btn.description == \"AC\":\n",
- " output.value = \"\"\n",
- " elif btn.description == \"del\":\n",
- " output.value = output.value[:-1]\n",
- " else:\n",
- " output.value = output.value + btn.description\n",
- "\n",
- "def mk_btn(description):\n",
- " btn = ipw.Button(description=description, layout=ipw.Layout(width=\"45px\"))\n",
- " btn.on_click(on_click)\n",
- " return btn\n",
- "\n",
- "row0 = ipw.HBox([mk_btn(d) for d in (\"(\", \")\", \"del\", \"AC\")])\n",
- "row1 = ipw.HBox([mk_btn(d) for d in (\"7\", \"8\", \"9\", \" / \")])\n",
- "row2 = ipw.HBox([mk_btn(d) for d in (\"4\", \"5\", \"6\", \" * \")])\n",
- "row3 = ipw.HBox([mk_btn(d) for d in (\"1\", \"2\", \"3\", \" - \")])\n",
- "row4 = ipw.HBox([mk_btn(d) for d in (\"0\", \".\", \"=\", \" + \")])\n",
- "ipw.VBox((output, row0, row1, row2, row3, row4))"
- ]
- }
- ],
- "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": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/001-Jupyter/002-JupyterExtensions/CodeFormatter.ipynb b/001-Jupyter/002-JupyterExtensions/CodeFormatter.ipynb
deleted file mode 100644
index e88a7dcf474f20d8d73544fd4a2efb1a5c1eb27f..0000000000000000000000000000000000000000
--- a/001-Jupyter/002-JupyterExtensions/CodeFormatter.ipynb
+++ /dev/null
@@ -1,80 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- " # Press \"Format notebook\" or in the menu \"Edit -> Apply ... Formatter\"\n",
- " # useless comment\n",
- "import requests # useless comment\n",
- "\n",
- "headers = {\n",
- " 'Referer': 'https://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236&DB_Short_Name=On-Time',\n",
- " 'Origin': 'https://www.transtats.bts.gov',\n",
- " 'Content-Type': 'application/x-www-form-urlencoded',\n",
- "}\n",
- "\n",
- "params = (\n",
- " ('Table_ID', '236'),\n",
- " ('Has_Group', '3'), ('Is_Zipped', '0'),\n",
- ")\n",
- "\n",
- "with open('modern-1-url.txt', encoding='utf-8') as f:\n",
- " data = f.read().strip()\n",
- "\n",
- "os.makedirs('data', exist_ok=True)\n",
- "\n",
- "\n",
- "import pandas as pd\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "def read(fp):\n",
- " df = (pd.read_csv(fp)\n",
- " .rename(columns=str.lower) .drop('unnamed: 36', axis=1) .pipe(extract_city_name) .pipe(time_to_datetime, ['dep_time', 'arr_time', 'crs_arr_time', 'crs_dep_time'])\n",
- " .assign(fl_date=lambda x: pd.to_datetime(x['fl_date']),\n",
- " dest=lambda x: pd.Categorical(x['dest']),\n",
- " origin=lambda x: pd.Categorical(x['origin']), tail_num=lambda x: pd.Categorical(x['tail_num']), unique_carrier=lambda x: pd.Categorical(x['unique_carrier']),\n",
- " cancellation_code=lambda x: pd.Categorical(x['cancellation_code'])))\n",
- " return df\n",
- "\n",
- "\n",
- "def extract_city_name(df:pd.DataFrame) -> pd.DataFrame:\n",
- " '''\n",
- " Chicago, IL -> Chicago for origin_city_name and dest_city_name\n",
- " '''\n",
- " cols = ['origin_city_name', 'dest_city_name']\n",
- " city = df[cols].apply(lambda x: x.str.extract(\"(.*), \\w{2}\", expand=False))\n",
- " df = df.copy()\n",
- " df[['origin_city_name', 'dest_city_name']] = city\n",
- " return df\n"
- ]
- }
- ],
- "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": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/001-Jupyter/002-JupyterExtensions/Dask_JURECA.ipynb b/001-Jupyter/002-JupyterExtensions/Dask_JURECA.ipynb
deleted file mode 100644
index 00a9185badac0df4030dc259d248d20dc9c720ab..0000000000000000000000000000000000000000
--- a/001-Jupyter/002-JupyterExtensions/Dask_JURECA.ipynb
+++ /dev/null
@@ -1,390 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Dask Extension"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## If you have problems with this tutorial, try to download the Notebook."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "!wget https://jupyter-jsc.fz-juelich.de/static/files/Dask_JURECA.ipynb"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This notebook will give you a short introduction into the Dask Extension on JURECA. It allows you to run Jobs on the compute nodes, even if your JupyterLab is running interactively on the login node. \n",
- "First you have to define on which project and partition it should be running."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "queue = \"batch\" # batch, gpus, develgpus, etc.\n",
- "project = \"zam\" # your project: zam, training19xx, etc."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Monte-Carlo Estimate of $\\pi$\n",
- "\n",
- "We want to estimate the number $\\pi$ using a [Monte-Carlo method](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods) exploiting that the area of a quarter circle of unit radius is $\\pi/4$ and that hence the probability of any randomly chosen point in a unit square to lie in a unit circle centerd at a corner of the unit square is $\\pi/4$ as well. So for N randomly chosen pairs $(x, y)$ with $x\\in[0, 1)$ and $y\\in[0, 1)$, we count the number $N_{circ}$ of pairs that also satisfy $(x^2 + y^2) < 1$ and estimage $\\pi \\approx 4 \\cdot N_{circ} / N$.\n",
- "\n",
- "[<img src=\"https://upload.wikimedia.org/wikipedia/commons/8/84/Pi_30K.gif\" \n",
- " width=\"50%\" \n",
- " align=top\n",
- " alt=\"PI monte-carlo estimate\">](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Core Lessons\n",
- "\n",
- "- setting up SLURM (and other jobqueue) clusters\n",
- "- Scaling clusters\n",
- "- Adaptive clusters"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Set up a Slurm cluster\n",
- "\n",
- "We'll create a SLURM cluster and have a look at the job-script used to start workers on the HPC scheduler."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import dask\n",
- "from dask.distributed import Client\n",
- "from dask_jobqueue import SLURMCluster\n",
- "import os\n",
- "\n",
- "cluster = SLURMCluster(\n",
- " cores=24,\n",
- " processes=2,\n",
- " memory=\"100GB\",\n",
- " shebang='#!/usr/bin/env bash',\n",
- " queue=queue,\n",
- " scheduler_options={\"dashboard_address\": \":56755\"},\n",
- " walltime=\"00:30:00\",\n",
- " local_directory='/tmp',\n",
- " death_timeout=\"15s\",\n",
- " interface=\"ib0\",\n",
- " log_directory=f'{os.environ[\"HOME\"]}/dask_jobqueue_logs/',\n",
- " project=project)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(cluster.job_script())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "client = Client(cluster)\n",
- "client"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## You can visit the Dask Dashboard at the following url: \n",
- "```\n",
- "https://jupyter-jsc.fz-juelich.de/user/<user_name>/<lab_name>/proxy/<port>/status\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## You can integrate it into your JupyterLab environment by putting the link into the Dask Extension"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "![\"Dask\"](https://zam10183.zam.kfa-juelich.de/hub/static/images/dask2.png \"dask\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Afterwards you can press on the orange buttons to open a new tab in your JupyterLab Environment."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Scale the cluster to two nodes\n",
- "\n",
- "A look at the Dashboard reveals that there are no workers in the clusetr. Let's start 4 workers (in 2 SLURM jobs).\n",
- "\n",
- "For the distiction between _workers_ and _jobs_, see [the Dask jobqueue docs](https://jobqueue.dask.org/en/latest/howitworks.html#workers-vs-jobs)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "cluster.scale(4) # scale to 4 _workers_"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## The Monte Carlo Method"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import dask.array as da\n",
- "import numpy as np\n",
- "\n",
- "\n",
- "def calc_pi_mc(size_in_bytes, chunksize_in_bytes=200e6):\n",
- " \"\"\"Calculate PI using a Monte Carlo estimate.\"\"\"\n",
- "\n",
- " size = int(size_in_bytes / 8)\n",
- " chunksize = int(chunksize_in_bytes / 8)\n",
- "\n",
- " xy = da.random.uniform(0, 1, size=(size / 2, 2), chunks=(chunksize / 2, 2))\n",
- "\n",
- " in_circle = (xy ** 2).sum(axis=-1) < 1\n",
- " pi = 4 * in_circle.mean()\n",
- "\n",
- " return pi\n",
- "\n",
- "\n",
- "def print_pi_stats(size, pi, time_delta, num_workers):\n",
- " \"\"\"Print pi, calculate offset from true value, and print some stats.\"\"\"\n",
- " print(\n",
- " f\"{size / 1e9} GB\\n\"\n",
- " f\"\\tMC pi: {pi : 13.11f}\"\n",
- " f\"\\tErr: {abs(pi - np.pi) : 10.3e}\\n\"\n",
- " f\"\\tWorkers: {num_workers}\"\n",
- " f\"\\t\\tTime: {time_delta : 7.3f}s\"\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## The actual calculations\n",
- "\n",
- "We loop over different volumes of double-precision random numbers and estimate $\\pi$ as described above."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from time import time, sleep"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for size in (1e9 * n for n in (1, 10, 100)):\n",
- "\n",
- " start = time()\n",
- " pi = calc_pi_mc(size).compute()\n",
- " elaps = time() - start\n",
- "\n",
- " print_pi_stats(\n",
- " size, pi, time_delta=elaps, num_workers=len(cluster.scheduler.workers)\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Scaling the Cluster to twice its size\n",
- "\n",
- "We increase the number of workers by 2 and the re-run the experiments."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "new_num_workers = 2 * len(cluster.scheduler.workers)\n",
- "\n",
- "print(f\"Scaling from {len(cluster.scheduler.workers)} to {new_num_workers} workers.\")\n",
- "\n",
- "cluster.scale(new_num_workers)\n",
- "\n",
- "sleep(10)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "client"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Re-run same experiments with doubled cluster"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for size in (1e9 * n for n in (1, 10, 100)):\n",
- "\n",
- " start = time()\n",
- " pi = calc_pi_mc(size).compute()\n",
- " elaps = time() - start\n",
- "\n",
- " print_pi_stats(\n",
- " size, pi, time_delta=elaps, num_workers=len(cluster.scheduler.workers)\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Automatically Scaling the Cluster\n",
- "\n",
- "We want each calculation to take only a few seconds. Dask will try to add more workers to the cluster when workloads are high and remove workers when idling.\n",
- "\n",
- "_**Watch** how the cluster will scale down to the minimum a few seconds after being made adaptive._"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ca = cluster.adapt(minimum=4, maximum=100)\n",
- "\n",
- "sleep(4) # Allow for scale-down"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "client"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Repeat the calculation from above with larger work loads\n",
- "\n",
- "(And watch the dash board!)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for size in (n * 1e9 for n in (1, 10, 100)):\n",
- "\n",
- " start = time()\n",
- " pi = calc_pi_mc(size, min(size / 1000, 500e6)).compute()\n",
- " elaps = time() - start\n",
- "\n",
- " print_pi_stats(\n",
- " size, pi, time_delta=elaps, num_workers=len(cluster.scheduler.workers)\n",
- " )\n",
- "\n",
- " sleep(20) # allow for scale-down time"
- ]
- }
- ],
- "metadata": {
- "anaconda-cloud": {},
- "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": "3.7.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/001-Jupyter/002-JupyterExtensions/Dask_JURON.ipynb b/001-Jupyter/002-JupyterExtensions/Dask_JURON.ipynb
deleted file mode 100644
index 5db9fd332a703526a52d75fee708dca674e630fc..0000000000000000000000000000000000000000
--- a/001-Jupyter/002-JupyterExtensions/Dask_JURON.ipynb
+++ /dev/null
@@ -1,382 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Dask Extension"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## If you have problems with this tutorial, try to download the Notebook."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "!wget https://jupyter-jsc.fz-juelich.de/static/files/Dask_JURON.ipynb"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This notebook will give you a short introduction into the Dask Extension on JURON. It allows you to run Jobs on the compute nodes, even if your JupyterLab is running interactively on the login node. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Monte-Carlo Estimate of $\\pi$\n",
- "\n",
- "We want to estimate the number $\\pi$ using a [Monte-Carlo method](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods) exploiting that the area of a quarter circle of unit radius is $\\pi/4$ and that hence the probability of any randomly chosen point in a unit square to lie in a unit circle centerd at a corner of the unit square is $\\pi/4$ as well. So for N randomly chosen pairs $(x, y)$ with $x\\in[0, 1)$ and $y\\in[0, 1)$, we count the number $N_{circ}$ of pairs that also satisfy $(x^2 + y^2) < 1$ and estimage $\\pi \\approx 4 \\cdot N_{circ} / N$.\n",
- "\n",
- "[<img src=\"https://upload.wikimedia.org/wikipedia/commons/8/84/Pi_30K.gif\" \n",
- " width=\"50%\" \n",
- " align=top\n",
- " alt=\"PI monte-carlo estimate\">](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Core Lessons\n",
- "\n",
- "- setting up SLURM (and other jobqueue) clusters\n",
- "- Scaling clusters\n",
- "- Adaptive clusters"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Set up a Slurm cluster\n",
- "\n",
- "We'll create a SLURM cluster and have a look at the job-script used to start workers on the HPC scheduler."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import dask\n",
- "from dask.distributed import Client\n",
- "from dask_jobqueue import LSFCluster\n",
- "import os\n",
- "\n",
- "dask.config.set({\"jobqueue.lsf.use-stdin\": True})\n",
- "cluster = LSFCluster(\n",
- " queue=\"normal\",\n",
- " walltime=\"60\",\n",
- " ncpus=2,\n",
- " host=\"192.168.45.25\",\n",
- " scheduler_options={\"dashboard_address\": \"0.0.0.0:56755\"},\n",
- " death_timeout=\"15s\",\n",
- " mem=4 * 1024 * 1024 * 1024,\n",
- " log_directory=\"{}/dask_jobqueue_logs\".format(os.getenv(\"HOME\")),\n",
- " cores=4,\n",
- " locals_directory=\"/tmp\",\n",
- " n_workers=4,\n",
- " memory=\"128GB\",\n",
- " usestd_in=True\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(cluster.job_script())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "client = Client(cluster)\n",
- "client"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## You can visit the Dask Dashboard at the following url: \n",
- "```\n",
- "https://jupyter-jsc.fz-juelich.de/user/<user_name>/<lab_name>/proxy/<port>/status\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## You can integrate it into your JupyterLab environment by putting the link into the Dask Extension"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "![\"Dask\"](https://zam10183.zam.kfa-juelich.de/hub/static/images/dask2.png \"dask\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Afterwards you can press on the orange buttons to open a new tab in your JupyterLab Environment."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Scale the cluster to two nodes\n",
- "\n",
- "A look at the Dashboard reveals that there are no workers in the clusetr. Let's start 4 workers (in 2 SLURM jobs).\n",
- "\n",
- "For the distiction between _workers_ and _jobs_, see [the Dask jobqueue docs](https://jobqueue.dask.org/en/latest/howitworks.html#workers-vs-jobs)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "cluster.scale(4) # scale to 4 _workers_"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## The Monte Carlo Method"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import dask.array as da\n",
- "import numpy as np\n",
- "\n",
- "\n",
- "def calc_pi_mc(size_in_bytes, chunksize_in_bytes=200e6):\n",
- " \"\"\"Calculate PI using a Monte Carlo estimate.\"\"\"\n",
- "\n",
- " size = int(size_in_bytes / 8)\n",
- " chunksize = int(chunksize_in_bytes / 8)\n",
- "\n",
- " xy = da.random.uniform(0, 1, size=(size / 2, 2), chunks=(chunksize / 2, 2))\n",
- "\n",
- " in_circle = (xy ** 2).sum(axis=-1) < 1\n",
- " pi = 4 * in_circle.mean()\n",
- "\n",
- " return pi\n",
- "\n",
- "\n",
- "def print_pi_stats(size, pi, time_delta, num_workers):\n",
- " \"\"\"Print pi, calculate offset from true value, and print some stats.\"\"\"\n",
- " print(\n",
- " f\"{size / 1e9} GB\\n\"\n",
- " f\"\\tMC pi: {pi : 13.11f}\"\n",
- " f\"\\tErr: {abs(pi - np.pi) : 10.3e}\\n\"\n",
- " f\"\\tWorkers: {num_workers}\"\n",
- " f\"\\t\\tTime: {time_delta : 7.3f}s\"\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## The actual calculations\n",
- "\n",
- "We loop over different volumes of double-precision random numbers and estimate $\\pi$ as described above."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from time import time, sleep"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for size in (1e9 * n for n in (1, 10, 100)):\n",
- "\n",
- " start = time()\n",
- " pi = calc_pi_mc(size).compute()\n",
- " elaps = time() - start\n",
- "\n",
- " print_pi_stats(\n",
- " size, pi, time_delta=elaps, num_workers=len(cluster.scheduler.workers)\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Scaling the Cluster to twice its size\n",
- "\n",
- "We increase the number of workers by 2 and the re-run the experiments."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "new_num_workers = 2 * len(cluster.scheduler.workers)\n",
- "\n",
- "print(f\"Scaling from {len(cluster.scheduler.workers)} to {new_num_workers} workers.\")\n",
- "\n",
- "cluster.scale(new_num_workers)\n",
- "\n",
- "sleep(10)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "client"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Re-run same experiments with doubled cluster"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for size in (1e9 * n for n in (1, 10, 100)):\n",
- "\n",
- " start = time()\n",
- " pi = calc_pi_mc(size).compute()\n",
- " elaps = time() - start\n",
- "\n",
- " print_pi_stats(\n",
- " size, pi, time_delta=elaps, num_workers=len(cluster.scheduler.workers)\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Automatically Scaling the Cluster\n",
- "\n",
- "We want each calculation to take only a few seconds. Dask will try to add more workers to the cluster when workloads are high and remove workers when idling.\n",
- "\n",
- "_**Watch** how the cluster will scale down to the minimum a few seconds after being made adaptive._"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ca = cluster.adapt(minimum=4, maximum=100)\n",
- "\n",
- "sleep(4) # Allow for scale-down"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "client"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Repeat the calculation from above with larger work loads\n",
- "\n",
- "(And watch the dash board!)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for size in (n * 1e9 for n in (1, 10, 100)):\n",
- "\n",
- " start = time()\n",
- " pi = calc_pi_mc(size, min(size / 1000, 500e6)).compute()\n",
- " elaps = time() - start\n",
- "\n",
- " print_pi_stats(\n",
- " size, pi, time_delta=elaps, num_workers=len(cluster.scheduler.workers)\n",
- " )\n",
- "\n",
- " sleep(20) # allow for scale-down time"
- ]
- }
- ],
- "metadata": {
- "anaconda-cloud": {},
- "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": "3.7.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/001-Jupyter/002-JupyterExtensions/Dask_JUWELS.ipynb b/001-Jupyter/002-JupyterExtensions/Dask_JUWELS.ipynb
deleted file mode 100644
index 1e9345a4b47f24f6d89fd2d37b61f6d43e60409e..0000000000000000000000000000000000000000
--- a/001-Jupyter/002-JupyterExtensions/Dask_JUWELS.ipynb
+++ /dev/null
@@ -1,392 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Dask Extension"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This notebook will give you a short introduction into the Dask Extension on JURECA. It allows you to run Jobs on the compute nodes, even if your JupyterLab is running interactively on the login node. \n",
- "First you have to define on which project and partition it should be running."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "queue = \"batch\" # batch, gpus, develgpus, etc.\n",
- "project = \"zam\" # your project: zam, training19xx, etc."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Monte-Carlo Estimate of $\\pi$\n",
- "\n",
- "We want to estimate the number $\\pi$ using a [Monte-Carlo method](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods) exploiting that the area of a quarter circle of unit radius is $\\pi/4$ and that hence the probability of any randomly chosen point in a unit square to lie in a unit circle centerd at a corner of the unit square is $\\pi/4$ as well. So for N randomly chosen pairs $(x, y)$ with $x\\in[0, 1)$ and $y\\in[0, 1)$, we count the number $N_{circ}$ of pairs that also satisfy $(x^2 + y^2) < 1$ and estimage $\\pi \\approx 4 \\cdot N_{circ} / N$.\n",
- "\n",
- "[<img src=\"https://upload.wikimedia.org/wikipedia/commons/8/84/Pi_30K.gif\" \n",
- " width=\"50%\" \n",
- " align=top\n",
- " alt=\"PI monte-carlo estimate\">](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Core Lessons\n",
- "\n",
- "- setting up SLURM (and other jobqueue) clusters\n",
- "- Scaling clusters\n",
- "- Adaptive clusters"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Set up a Slurm cluster\n",
- "\n",
- "We'll create a SLURM cluster and have a look at the job-script used to start workers on the HPC scheduler."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import dask\n",
- "from dask.distributed import Client\n",
- "from dask_jobqueue import SLURMCluster\n",
- "import os\n",
- "\n",
- "cluster = SLURMCluster(\n",
- " cores=24,\n",
- " processes=2,\n",
- " memory=\"100GB\",\n",
- " shebang=\"#!/usr/bin/env bash\",\n",
- " queue=queue,\n",
- " scheduler_options={\"dashboard_address\": \":56764\"},\n",
- " walltime=\"00:30:00\",\n",
- " local_directory=\"/tmp\",\n",
- " death_timeout=\"15s\",\n",
- " interface=\"ib0\",\n",
- " log_directory=f'{os.environ[\"HOME\"]}/dask_jobqueue_logs/',\n",
- " project=project,\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(cluster.job_script())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "client = Client(cluster)\n",
- "client"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## You can visit the Dask Dashboard at the following url: \n",
- "```\n",
- "https://jupyter-jsc.fz-juelich.de/user/<user_name>/<lab_name>/proxy/<port>/status\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## You can integrate it into your JupyterLab environment by putting the link into the Dask Extension"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "![\"Dask\"](https://jupyter-jsc.fz-juelich.de/hub/static/images/dask2.png \"dask\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Afterwards you can press on the orange buttons to open a new tab in your JupyterLab Environment."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Scale the cluster to two nodes\n",
- "\n",
- "A look at the Dashboard reveals that there are no workers in the clusetr. Let's start 4 workers (in 2 SLURM jobs).\n",
- "\n",
- "For the distiction between _workers_ and _jobs_, see [the Dask jobqueue docs](https://jobqueue.dask.org/en/latest/howitworks.html#workers-vs-jobs)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "cluster.scale(4) # scale to 4 _workers_"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## The Monte Carlo Method"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import dask.array as da\n",
- "import numpy as np\n",
- "\n",
- "\n",
- "def calc_pi_mc(size_in_bytes, chunksize_in_bytes=200e6):\n",
- " \"\"\"Calculate PI using a Monte Carlo estimate.\"\"\"\n",
- "\n",
- " size = int(size_in_bytes / 8)\n",
- " chunksize = int(chunksize_in_bytes / 8)\n",
- "\n",
- " xy = da.random.uniform(0, 1, size=(size / 2, 2), chunks=(chunksize / 2, 2))\n",
- "\n",
- " in_circle = (xy ** 2).sum(axis=-1) < 1\n",
- " pi = 4 * in_circle.mean()\n",
- "\n",
- " return pi\n",
- "\n",
- "\n",
- "def print_pi_stats(size, pi, time_delta, num_workers):\n",
- " \"\"\"Print pi, calculate offset from true value, and print some stats.\"\"\"\n",
- " print(\n",
- " f\"{size / 1e9} GB\\n\"\n",
- " f\"\\tMC pi: {pi : 13.11f}\"\n",
- " f\"\\tErr: {abs(pi - np.pi) : 10.3e}\\n\"\n",
- " f\"\\tWorkers: {num_workers}\"\n",
- " f\"\\t\\tTime: {time_delta : 7.3f}s\"\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## The actual calculations\n",
- "\n",
- "We loop over different volumes of double-precision random numbers and estimate $\\pi$ as described above."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from time import time, sleep"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for size in (1e9 * n for n in (1, 10, 100)):\n",
- "\n",
- " start = time()\n",
- " pi = calc_pi_mc(size).compute()\n",
- " elaps = time() - start\n",
- "\n",
- " print_pi_stats(\n",
- " size, pi, time_delta=elaps, num_workers=len(cluster.scheduler.workers)\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Is it running?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To check if something has been started for you just use the following command in a terminal: \n",
- "```\n",
- "squeue | grep ${USER}\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Scaling the Cluster to twice its size\n",
- "\n",
- "We increase the number of workers by 2 and the re-run the experiments."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "new_num_workers = 2 * len(cluster.scheduler.workers)\n",
- "\n",
- "print(f\"Scaling from {len(cluster.scheduler.workers)} to {new_num_workers} workers.\")\n",
- "\n",
- "cluster.scale(new_num_workers)\n",
- "\n",
- "sleep(10)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "client"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Re-run same experiments with doubled cluster"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for size in (1e9 * n for n in (1, 10, 100)):\n",
- "\n",
- " start = time()\n",
- " pi = calc_pi_mc(size).compute()\n",
- " elaps = time() - start\n",
- "\n",
- " print_pi_stats(\n",
- " size, pi, time_delta=elaps, num_workers=len(cluster.scheduler.workers)\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Automatically Scaling the Cluster\n",
- "\n",
- "We want each calculation to take only a few seconds. Dask will try to add more workers to the cluster when workloads are high and remove workers when idling.\n",
- "\n",
- "_**Watch** how the cluster will scale down to the minimum a few seconds after being made adaptive._"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ca = cluster.adapt(minimum=4, maximum=100)\n",
- "\n",
- "sleep(4) # Allow for scale-down"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "client"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Repeat the calculation from above with larger work loads\n",
- "\n",
- "(And watch the dash board!)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for size in (n * 1e9 for n in (1, 10, 100)):\n",
- "\n",
- " start = time()\n",
- " pi = calc_pi_mc(size, min(size / 1000, 500e6)).compute()\n",
- " elaps = time() - start\n",
- "\n",
- " print_pi_stats(\n",
- " size, pi, time_delta=elaps, num_workers=len(cluster.scheduler.workers)\n",
- " )\n",
- "\n",
- " sleep(20) # allow for scale-down time"
- ]
- }
- ],
- "metadata": {
- "anaconda-cloud": {},
- "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": "3.8.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/001-Jupyter/002-JupyterExtensions/Leaflet.ipynb b/001-Jupyter/002-JupyterExtensions/Leaflet.ipynb
deleted file mode 100644
index 440026a7e609367c3f3e8ed686e6ea8399b9b18a..0000000000000000000000000000000000000000
--- a/001-Jupyter/002-JupyterExtensions/Leaflet.ipynb
+++ /dev/null
@@ -1,182 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "from __future__ import print_function"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "from ipyleaflet import *"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "44421bfb7b9643c6a551e2f985765f06",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Map(basemap={'url': 'http://{s}.tile.openstreetmap.se/hydda/full/{z}/{x}/{y}.png', 'max_zoom': 18, 'attributio…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "m = Map(center=(52, 10), zoom=8, basemap=basemaps.Hydda.Full)\n",
- "m"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "strata_all = basemap_to_tiles(basemaps.Strava.All)\n",
- "m.add_layer(strata_all)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "eb6a3ffc5aaa4fe6a4bfff2d09ae271b",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Label(value='')"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# mouse interaction handling\n",
- "\n",
- "from ipywidgets import Label\n",
- "\n",
- "label = Label()\n",
- "display(label)\n",
- "\n",
- "def handle_interaction(**kwargs):\n",
- " if kwargs.get('type') == 'mousemove':\n",
- " label.value = str(kwargs.get('coordinates'))\n",
- "\n",
- "m.on_interaction(handle_interaction)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "strata_water = basemap_to_tiles(basemaps.Strava.Water)\n",
- "m.substitute_layer(strata_all, strata_water)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Adding an overlay layer\n",
- "import json\n",
- "\n",
- "with open('./europe_110.geo.json') as f:\n",
- " data = json.load(f)\n",
- "for feature in data['features']:\n",
- " feature['properties']['style'] = {\n",
- " 'color': 'grey',\n",
- " 'weight': 1,\n",
- " 'fillColor': 'grey',\n",
- " 'fillOpacity': 0.5\n",
- " }\n",
- "geo = GeoJSON(data=data, hover_style={'fillColor': 'red'}, name='Countries')\n",
- "m.add_layer(geo)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Adding the control\n",
- "m.add_control(LayersControl())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "637b77852b1248eca4fbd6042cb52de5",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "HBox(children=(Map(basemap={'url': 'http://{s}.tile.openstreetmap.se/hydda/full/{z}/{x}/{y}.png', 'max_zoom': …"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Creating two maps side by side\n",
- "import ipywidgets\n",
- " \n",
- "ipywidgets.HBox([m, Map(center=[43.6, 1.44], zoom=10)])"
- ]
- }
- ],
- "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": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/001-Jupyter/002-JupyterExtensions/Sidecar.ipynb b/001-Jupyter/002-JupyterExtensions/Sidecar.ipynb
deleted file mode 100644
index 0b0cffe2557cf8a454b4eb5d753e896c7e0eeb94..0000000000000000000000000000000000000000
--- a/001-Jupyter/002-JupyterExtensions/Sidecar.ipynb
+++ /dev/null
@@ -1,59 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "from sidecar import Sidecar\n",
- "from ipyleaflet import Map, basemaps, basemap_to_tiles"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "m = Map(center=(52,10), zoom=8, basemap=basemaps.CartoDB.DarkMatter)\n",
- "\n",
- "strata_all = basemap_to_tiles(basemaps.Strava.All)\n",
- "m.add_layer(strata_all)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "sc = Sidecar(title='Some title')\n",
- "\n",
- "with sc:\n",
- " display(m)"
- ]
- }
- ],
- "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": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/001-Jupyter/002-JupyterExtensions/ThreeJS.ipynb b/001-Jupyter/002-JupyterExtensions/ThreeJS.ipynb
deleted file mode 100644
index 0288ce04bbf00e6996055204f04f68e4ef643840..0000000000000000000000000000000000000000
--- a/001-Jupyter/002-JupyterExtensions/ThreeJS.ipynb
+++ /dev/null
@@ -1,491 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "from pythreejs import *\n",
- "import numpy as np\n",
- "from IPython.display import display\n",
- "from ipywidgets import HTML, Text, Output, VBox\n",
- "from traitlets import link, dlink"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Simple sphere and text"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "6179c806fbc5462bbd7701d998f58b4f",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Renderer(camera=PerspectiveCamera(children=(DirectionalLight(color='white', intensity=0.5, position=(3.0, 5.0,…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "ball = Mesh(geometry=SphereGeometry(radius=1, widthSegments=32, heightSegments=24), \n",
- " material=MeshLambertMaterial(color='red'),\n",
- " position=[2, 1, 0])\n",
- "\n",
- "c = PerspectiveCamera(position=[0, 5, 5], up=[0, 1, 0],\n",
- " children=[DirectionalLight(color='white', position=[3, 5, 1], intensity=0.5)])\n",
- "\n",
- "scene = Scene(children=[ball, c, AmbientLight(color='#777777')])\n",
- "\n",
- "renderer = Renderer(camera=c, \n",
- " scene=scene, \n",
- " controls=[OrbitControls(controlling=c)])\n",
- "display(renderer)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "ball.scale = (0.5,) * 3"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "import time, math\n",
- "ball.material.color = '#4400dd'\n",
- "for i in range(1, 150, 2):\n",
- " ball.scale = (i / 100.,) * 3\n",
- " ball.position = [math.cos(i / 10.), math.sin(i / 50.), i / 100.]\n",
- " time.sleep(.05)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Clickable Surface\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "ename": "ModuleNotFoundError",
- "evalue": "No module named 'skimage'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-5-ea9b46f5dc29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m surf = Mesh(geometry=surf_g,\n\u001b[0;32m---> 21\u001b[0;31m material=MeshLambertMaterial(map=height_texture(z[::-1], 'YlGnBu_r')))\n\u001b[0m\u001b[1;32m 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m surfgrid = SurfaceGrid(geometry=surf_g, material=LineBasicMaterial(color='black'),\n",
- "\u001b[0;32m/usr/local/software/jureca/Stages/Devel-2019a/software/Jupyter/2019a-rc15-gcccoremkl-8.3.0-2019.3.199-Python-3.6.8/lib/python3.6/site-packages/pythreejs/pythreejs.py\u001b[0m in \u001b[0;36mheight_texture\u001b[0;34m(z, colormap)\u001b[0m\n\u001b[1;32m 235\u001b[0m \u001b[0;34m\"\"\"Create a texture corresponding to the heights in z and the given colormap.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 237\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mskimage\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mimg_as_ubyte\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 239\u001b[0m \u001b[0mcolormap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'skimage'"
- ]
- }
- ],
- "source": [
- "# Generate surface data:\n",
- "view_width = 600\n",
- "view_height = 400\n",
- "nx, ny = (20, 20)\n",
- "xmax=1\n",
- "x = np.linspace(-xmax, xmax, nx)\n",
- "y = np.linspace(-xmax, xmax, ny)\n",
- "xx, yy = np.meshgrid(x, y)\n",
- "z = xx ** 2 - yy ** 2\n",
- "#z[6,1] = float('nan')\n",
- "\n",
- "\n",
- "# Generate scene objects from data:\n",
- "surf_g = SurfaceGeometry(z=list(z[::-1].flat), \n",
- " width=2 * xmax,\n",
- " height=2 * xmax,\n",
- " width_segments=nx - 1,\n",
- " height_segments=ny - 1)\n",
- "\n",
- "surf = Mesh(geometry=surf_g,\n",
- " material=MeshLambertMaterial(map=height_texture(z[::-1], 'YlGnBu_r')))\n",
- "\n",
- "surfgrid = SurfaceGrid(geometry=surf_g, material=LineBasicMaterial(color='black'),\n",
- " position=[0, 0, 1e-2]) # Avoid overlap by lifting grid slightly\n",
- "\n",
- "# Set up picking bojects:\n",
- "hover_point = Mesh(geometry=SphereGeometry(radius=0.05),\n",
- " material=MeshLambertMaterial(color='hotpink'))\n",
- "\n",
- "click_picker = Picker(controlling=surf, event='dblclick')\n",
- "hover_picker = Picker(controlling=surf, event='mousemove')\n",
- "\n",
- "# Set up scene:\n",
- "key_light = DirectionalLight(color='white', position=[3, 5, 1], intensity=0.4)\n",
- "c = PerspectiveCamera(position=[0, 3, 3], up=[0, 0, 1], aspect=view_width / view_height,\n",
- " children=[key_light])\n",
- "\n",
- "scene = Scene(children=[surf, c, surfgrid, hover_point, AmbientLight(intensity=0.8)])\n",
- "\n",
- "renderer = Renderer(camera=c, scene=scene,\n",
- " width=view_width, height=view_height,\n",
- " controls=[OrbitControls(controlling=c), click_picker, hover_picker])\n",
- "\n",
- "\n",
- "# Set up picking responses:\n",
- "# Add a new marker when double-clicking:\n",
- "out = Output()\n",
- "def f(change):\n",
- " value = change['new']\n",
- " with out:\n",
- " print('Clicked on %s' % (value,))\n",
- " point = Mesh(geometry=SphereGeometry(radius=0.05), \n",
- " material=MeshLambertMaterial(color='red'),\n",
- " position=value)\n",
- " scene.add(point)\n",
- "\n",
- "click_picker.observe(f, names=['point'])\n",
- "\n",
- "# Have marker follow picker point:\n",
- "link((hover_point, 'position'), (hover_picker, 'point'))\n",
- "\n",
- "# Show picker point coordinates as a label:\n",
- "h = HTML()\n",
- "def g(change):\n",
- " h.value = 'Green point at (%.3f, %.3f, %.3f)' % tuple(change['new'])\n",
- "g({'new': hover_point.position})\n",
- "hover_picker.observe(g, names=['point'])\n",
- "\n",
- "display(VBox([h, renderer, out]))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "surf_g.z = list((-z[::-1]).flat)\n",
- "surf.material.map = height_texture(-z[::-1])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Design our own texture"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "from scipy import ndimage\n",
- "import matplotlib\n",
- "import matplotlib.pyplot as plt\n",
- "from skimage import img_as_ubyte \n",
- "\n",
- "jet = matplotlib.cm.get_cmap('jet')\n",
- "\n",
- "np.random.seed(int(1)) # start random number generator\n",
- "n = int(5) # starting points\n",
- "size = int(32) # size of image\n",
- "im = np.zeros((size,size)) # create zero image\n",
- "points = size*np.random.random((2, n**2)) # locations of seed values\n",
- "im[(points[0]).astype(np.int), (points[1]).astype(np.int)] = size # seed high values\n",
- "im = ndimage.gaussian_filter(im, sigma=size/(float(4)*n)) # smooth high values into surrounding areas\n",
- "im *= 1/np.max(im)# rescale to be in the range [0,1]\n",
- "rgba_im = img_as_ubyte(jet(im)) # convert the values to rgba image using the jet colormap\n",
- "\n",
- "t = DataTexture(data=rgba_im, format='RGBAFormat', width=size, height=size)\n",
- "\n",
- "geometry = SphereGeometry(radius=1, widthSegments=16, heightSegments=10)#TorusKnotGeometry(radius=2, radialSegments=200)\n",
- "material = MeshLambertMaterial(map=t)\n",
- "\n",
- "myobject = Mesh(geometry=geometry, material=material)\n",
- "c = PerspectiveCamera(position=[0, 3, 3], fov=40,\n",
- " children=[DirectionalLight(color='#ffffff', position=[3, 5, 1], intensity=0.5)])\n",
- "scene = Scene(children=[myobject, c, AmbientLight(color='#777777')])\n",
- "\n",
- "renderer = Renderer(camera=c, scene = scene, controls=[OrbitControls(controlling=c)], width=400, height=400)\n",
- "display(renderer)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Lines"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# On windows, linewidth of the material has no effect\n",
- "size = 4\n",
- "linesgeom = Geometry(vertices=[[0, 0, 0],\n",
- " [size, 0, 0],\n",
- " [0, 0, 0],\n",
- " [0, size, 0],\n",
- " [0, 0, 0],\n",
- " [0, 0, size]],\n",
- " colors = ['red', 'red', 'green', 'green', 'white', 'orange'])\n",
- "lines = Line(geometry=linesgeom, \n",
- " material=LineBasicMaterial(linewidth=5, vertexColors='VertexColors'), \n",
- " type='LinePieces',\n",
- " )\n",
- "scene = Scene(children=[\n",
- " lines,\n",
- " DirectionalLight(color='#ccaabb', position=[0,10,0]),\n",
- " AmbientLight(color='#cccccc'),\n",
- " ])\n",
- "c = PerspectiveCamera(position=[10, 10, 10])\n",
- "renderer = Renderer(camera=c, background='black', background_opacity=1, scene=scene, controls=[OrbitControls(controlling=c)],\n",
- " width=400, height=400)\n",
- "display(renderer)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Parametric Functions\n",
- "\n",
- "\n",
- "To use the ParametricGeometry class, you need to specify a javascript function as a string. The function should take two parameters that vary between 0 and 1, and a `THREE.Vector3(x,y,z)` that should be modified in place.\n",
- "\n",
- "If you want to build the surface in Python, you'll need to explicitly construct the vertices and faces and build a basic geometry from the vertices and faces."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "f = \"\"\"\n",
- "function f(origu, origv, out) {\n",
- " // scale u and v to the ranges I want: [0, 2*pi]\n",
- " var u = 2*Math.PI*origu;\n",
- " var v = 2*Math.PI*origv;\n",
- " \n",
- " var x = Math.sin(u);\n",
- " var y = Math.cos(v);\n",
- " var z = Math.cos(u+v);\n",
- " \n",
- " out.set(x,y,z)\n",
- "}\n",
- "\"\"\"\n",
- "surf_g = ParametricGeometry(func=f, slices=16, stacks=16);\n",
- "\n",
- "surf = Mesh(geometry=surf_g, material=MeshLambertMaterial(color='green', side='FrontSide'))\n",
- "surf2 = Mesh(geometry=surf_g, material=MeshLambertMaterial(color='yellow', side='BackSide'))\n",
- "c = PerspectiveCamera(position=[5, 5, 3], up=[0, 0, 1],\n",
- " children=[DirectionalLight(color='white',\n",
- " position=[3, 5, 1],\n",
- " intensity=0.6)])\n",
- "scene = Scene(children=[surf, surf2, c, AmbientLight(intensity=0.5)])\n",
- "renderer = Renderer(camera=c, scene=scene, controls=[OrbitControls(controlling=c)], width=400, height=400)\n",
- "display(renderer)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Indexed Geometries\n",
- "\n",
- "The PlainGeometry lets you specify vertices and faces for a surface."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from pythreejs import *\n",
- "from IPython.display import display\n",
- "\n",
- "vertices = [\n",
- " [0, 0, 0],\n",
- " [0, 0, 1],\n",
- " [0, 1, 0],\n",
- " [0, 1, 1],\n",
- " [1, 0, 0],\n",
- " [1, 0, 1],\n",
- " [1, 1, 0],\n",
- " [1, 1, 1]\n",
- "]\n",
- "\n",
- "faces = [\n",
- " [0, 1, 3],\n",
- " [0, 3, 2],\n",
- " [0, 2, 4],\n",
- " [2, 6, 4],\n",
- " [0, 4, 1],\n",
- " [1, 4, 5],\n",
- " [2, 3, 6],\n",
- " [3, 7, 6],\n",
- " [1, 5, 3],\n",
- " [3, 5, 7],\n",
- " [4, 6, 5],\n",
- " [5, 6, 7]\n",
- "]\n",
- "\n",
- "vertexcolors = ['#000000', '#0000ff', '#00ff00', '#ff0000',\n",
- " '#00ffff', '#ff00ff', '#ffff00', '#ffffff']\n",
- "\n",
- "# Map the vertex colors into the 'color' slot of the faces\n",
- "faces = [f + [None, [vertexcolors[i] for i in f], None] for f in faces]\n",
- "\n",
- "# Create the geometry:\n",
- "cubeGeometry = Geometry(vertices=vertices,\n",
- " faces=faces,\n",
- " colors=vertexcolors)\n",
- "# Calculate normals per face, for nice crisp edges:\n",
- "cubeGeometry.exec_three_obj_method('computeFaceNormals')\n",
- "\n",
- "# Create a mesh. Note that the material need to be told to use the vertex colors.\n",
- "myobjectCube = Mesh(\n",
- " geometry=cubeGeometry,\n",
- " material=MeshLambertMaterial(vertexColors='VertexColors'),\n",
- " position=[-0.5, -0.5, -0.5], # Center the cube\n",
- ")\n",
- "\n",
- "# Set up a scene and render it:\n",
- "cCube = PerspectiveCamera(position=[3, 3, 3], fov=20,\n",
- " children=[DirectionalLight(color='#ffffff', position=[-3, 5, 1], intensity=0.5)])\n",
- "sceneCube = Scene(children=[myobjectCube, cCube, AmbientLight(color='#dddddd')])\n",
- "\n",
- "rendererCube = Renderer(camera=cCube, background='black', background_opacity=1,\n",
- " scene=sceneCube, controls=[OrbitControls(controlling=cCube)])\n",
- "\n",
- "display(rendererCube)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Buffer Geometries\n",
- "\n",
- "The PlainBufferGeometry object uses several tricks to speed up both the transfer of data and the rendering of the data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from pythreejs import *\n",
- "import numpy as np\n",
- "from IPython.display import display\n",
- "\n",
- "vertices = np.asarray([\n",
- " [0, 0, 0],\n",
- " [0, 0, 1],\n",
- " [0, 1, 0],\n",
- " [0, 1, 1],\n",
- " [1, 0, 0],\n",
- " [1, 0, 1],\n",
- " [1, 1, 0],\n",
- " [1, 1, 1]\n",
- "], dtype='float32')\n",
- "\n",
- "faces = np.asarray([\n",
- " [0, 1, 3],\n",
- " [0, 3, 2],\n",
- " [0, 2, 4],\n",
- " [2, 6, 4],\n",
- " [0, 4, 1],\n",
- " [1, 4, 5],\n",
- " [2, 3, 6],\n",
- " [3, 7, 6],\n",
- " [1, 5, 3],\n",
- " [3, 5, 7],\n",
- " [4, 6, 5],\n",
- " [5, 6, 7]\n",
- "], dtype='uint16').ravel() # We need to flatten index array\n",
- "\n",
- "\n",
- "vertexcolors = np.asarray([(0,0,0), (0,0,1), (0,1,0), (1,0,0),\n",
- " (0,1,1), (1,0,1), (1,1,0), (1,1,1)], dtype='float32')\n",
- "\n",
- "cubeGeometry = BufferGeometry(attributes=dict(\n",
- " position=BufferAttribute(vertices, normalized=False),\n",
- " index=BufferAttribute(faces, normalized=False),\n",
- " color=BufferAttribute(vertexcolors),\n",
- "))\n",
- "\n",
- "myobjectCube = Mesh(\n",
- " geometry=cubeGeometry,\n",
- " material=MeshLambertMaterial(vertexColors='VertexColors'),\n",
- " position=[-0.5, -0.5, -0.5] # Center the cube\n",
- ")\n",
- "cCube = PerspectiveCamera(\n",
- " position=[3, 3, 3], fov=20,\n",
- " children=[DirectionalLight(color='#ffffff', position=[-3, 5, 1], intensity=0.5)])\n",
- "sceneCube = Scene(children=[myobjectCube, cCube, AmbientLight(color='#dddddd')])\n",
- "\n",
- "rendererCube = Renderer(camera=cCube, background='black', background_opacity=1,\n",
- " scene = sceneCube, controls=[OrbitControls(controlling=cCube)])\n",
- "\n",
- "display(rendererCube)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Note that there are no face normals logic for buffer geometries, as the attributes are *vertex* attributes. If you want to add sharp edges for a BufferGeometry, you then have to duplicate the vertices (i.e., don't use an index attribute), and calculate the normals yourself."
- ]
- }
- ],
- "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": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/001-Jupyter/002-JupyterExtensions/europe_110.geo.json b/001-Jupyter/002-JupyterExtensions/europe_110.geo.json
deleted file mode 100644
index 6f55fa72ed470437dddae32d39a20656ef72c5cd..0000000000000000000000000000000000000000
--- a/001-Jupyter/002-JupyterExtensions/europe_110.geo.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/001-Jupyter/002-JupyterExtensions/test.py b/001-Jupyter/002-JupyterExtensions/test.py
deleted file mode 100644
index a3ec619f7a5454e3adec480491438fee23e55773..0000000000000000000000000000000000000000
--- a/001-Jupyter/002-JupyterExtensions/test.py
+++ /dev/null
@@ -1,4 +0,0 @@
-def add(a, b):
- return a+b
-
-print(add(2,3))
\ No newline at end of file
diff --git a/001-Jupyter/002-JupyterExtensions/voila_basics.ipynb b/001-Jupyter/002-JupyterExtensions/voila_basics.ipynb
deleted file mode 100644
index 64237372f91ffff5ead663e9daf6a629f1516614..0000000000000000000000000000000000000000
--- a/001-Jupyter/002-JupyterExtensions/voila_basics.ipynb
+++ /dev/null
@@ -1,688 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# So easy, *voilà*!\n",
- "\n",
- "In this example notebook, we demonstrate how voila can render Jupyter notebooks with interactions requiring a roundtrip to the kernel."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Jupyter Widgets"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "43f7ac3144f143b6ab21d9895f938344",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "VBox(children=(FloatSlider(value=4.0, description='$x$'), FloatText(value=0.0, description='$x^2$', disabled=T…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import ipywidgets as widgets\n",
- "\n",
- "slider = widgets.FloatSlider(description='$x$', value=4)\n",
- "text = widgets.FloatText(disabled=True, description='$x^2$')\n",
- "\n",
- "def compute(*ignore):\n",
- " text.value = str(slider.value ** 2)\n",
- "\n",
- "slider.observe(compute, 'value')\n",
- "\n",
- "widgets.VBox([slider, text])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Basic outputs of code cells"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>sepal_length</th>\n",
- " <th>sepal_width</th>\n",
- " <th>petal_length</th>\n",
- " <th>petal_width</th>\n",
- " <th>species</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>5.1</td>\n",
- " <td>3.5</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>4.9</td>\n",
- " <td>3.0</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>4.7</td>\n",
- " <td>3.2</td>\n",
- " <td>1.3</td>\n",
- " <td>0.2</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4.6</td>\n",
- " <td>3.1</td>\n",
- " <td>1.5</td>\n",
- " <td>0.2</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>5.0</td>\n",
- " <td>3.6</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>5.4</td>\n",
- " <td>3.9</td>\n",
- " <td>1.7</td>\n",
- " <td>0.4</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>4.6</td>\n",
- " <td>3.4</td>\n",
- " <td>1.4</td>\n",
- " <td>0.3</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>5.0</td>\n",
- " <td>3.4</td>\n",
- " <td>1.5</td>\n",
- " <td>0.2</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>4.4</td>\n",
- " <td>2.9</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>4.9</td>\n",
- " <td>3.1</td>\n",
- " <td>1.5</td>\n",
- " <td>0.1</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>5.4</td>\n",
- " <td>3.7</td>\n",
- " <td>1.5</td>\n",
- " <td>0.2</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>4.8</td>\n",
- " <td>3.4</td>\n",
- " <td>1.6</td>\n",
- " <td>0.2</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>4.8</td>\n",
- " <td>3.0</td>\n",
- " <td>1.4</td>\n",
- " <td>0.1</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>4.3</td>\n",
- " <td>3.0</td>\n",
- " <td>1.1</td>\n",
- " <td>0.1</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>5.8</td>\n",
- " <td>4.0</td>\n",
- " <td>1.2</td>\n",
- " <td>0.2</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15</th>\n",
- " <td>5.7</td>\n",
- " <td>4.4</td>\n",
- " <td>1.5</td>\n",
- " <td>0.4</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16</th>\n",
- " <td>5.4</td>\n",
- " <td>3.9</td>\n",
- " <td>1.3</td>\n",
- " <td>0.4</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>17</th>\n",
- " <td>5.1</td>\n",
- " <td>3.5</td>\n",
- " <td>1.4</td>\n",
- " <td>0.3</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>18</th>\n",
- " <td>5.7</td>\n",
- " <td>3.8</td>\n",
- " <td>1.7</td>\n",
- " <td>0.3</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>19</th>\n",
- " <td>5.1</td>\n",
- " <td>3.8</td>\n",
- " <td>1.5</td>\n",
- " <td>0.3</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>20</th>\n",
- " <td>5.4</td>\n",
- " <td>3.4</td>\n",
- " <td>1.7</td>\n",
- " <td>0.2</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>21</th>\n",
- " <td>5.1</td>\n",
- " <td>3.7</td>\n",
- " <td>1.5</td>\n",
- " <td>0.4</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>22</th>\n",
- " <td>4.6</td>\n",
- " <td>3.6</td>\n",
- " <td>1.0</td>\n",
- " <td>0.2</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>23</th>\n",
- " <td>5.1</td>\n",
- " <td>3.3</td>\n",
- " <td>1.7</td>\n",
- " <td>0.5</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>24</th>\n",
- " <td>4.8</td>\n",
- " <td>3.4</td>\n",
- " <td>1.9</td>\n",
- " <td>0.2</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25</th>\n",
- " <td>5.0</td>\n",
- " <td>3.0</td>\n",
- " <td>1.6</td>\n",
- " <td>0.2</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>26</th>\n",
- " <td>5.0</td>\n",
- " <td>3.4</td>\n",
- " <td>1.6</td>\n",
- " <td>0.4</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>27</th>\n",
- " <td>5.2</td>\n",
- " <td>3.5</td>\n",
- " <td>1.5</td>\n",
- " <td>0.2</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>28</th>\n",
- " <td>5.2</td>\n",
- " <td>3.4</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29</th>\n",
- " <td>4.7</td>\n",
- " <td>3.2</td>\n",
- " <td>1.6</td>\n",
- " <td>0.2</td>\n",
- " <td>setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>120</th>\n",
- " <td>6.9</td>\n",
- " <td>3.2</td>\n",
- " <td>5.7</td>\n",
- " <td>2.3</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>121</th>\n",
- " <td>5.6</td>\n",
- " <td>2.8</td>\n",
- " <td>4.9</td>\n",
- " <td>2.0</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>122</th>\n",
- " <td>7.7</td>\n",
- " <td>2.8</td>\n",
- " <td>6.7</td>\n",
- " <td>2.0</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>123</th>\n",
- " <td>6.3</td>\n",
- " <td>2.7</td>\n",
- " <td>4.9</td>\n",
- " <td>1.8</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>124</th>\n",
- " <td>6.7</td>\n",
- " <td>3.3</td>\n",
- " <td>5.7</td>\n",
- " <td>2.1</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>125</th>\n",
- " <td>7.2</td>\n",
- " <td>3.2</td>\n",
- " <td>6.0</td>\n",
- " <td>1.8</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>126</th>\n",
- " <td>6.2</td>\n",
- " <td>2.8</td>\n",
- " <td>4.8</td>\n",
- " <td>1.8</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>127</th>\n",
- " <td>6.1</td>\n",
- " <td>3.0</td>\n",
- " <td>4.9</td>\n",
- " <td>1.8</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>128</th>\n",
- " <td>6.4</td>\n",
- " <td>2.8</td>\n",
- " <td>5.6</td>\n",
- " <td>2.1</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>129</th>\n",
- " <td>7.2</td>\n",
- " <td>3.0</td>\n",
- " <td>5.8</td>\n",
- " <td>1.6</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>130</th>\n",
- " <td>7.4</td>\n",
- " <td>2.8</td>\n",
- " <td>6.1</td>\n",
- " <td>1.9</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>131</th>\n",
- " <td>7.9</td>\n",
- " <td>3.8</td>\n",
- " <td>6.4</td>\n",
- " <td>2.0</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>132</th>\n",
- " <td>6.4</td>\n",
- " <td>2.8</td>\n",
- " <td>5.6</td>\n",
- " <td>2.2</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>133</th>\n",
- " <td>6.3</td>\n",
- " <td>2.8</td>\n",
- " <td>5.1</td>\n",
- " <td>1.5</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>134</th>\n",
- " <td>6.1</td>\n",
- " <td>2.6</td>\n",
- " <td>5.6</td>\n",
- " <td>1.4</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>135</th>\n",
- " <td>7.7</td>\n",
- " <td>3.0</td>\n",
- " <td>6.1</td>\n",
- " <td>2.3</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>136</th>\n",
- " <td>6.3</td>\n",
- " <td>3.4</td>\n",
- " <td>5.6</td>\n",
- " <td>2.4</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>137</th>\n",
- " <td>6.4</td>\n",
- " <td>3.1</td>\n",
- " <td>5.5</td>\n",
- " <td>1.8</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>138</th>\n",
- " <td>6.0</td>\n",
- " <td>3.0</td>\n",
- " <td>4.8</td>\n",
- " <td>1.8</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>139</th>\n",
- " <td>6.9</td>\n",
- " <td>3.1</td>\n",
- " <td>5.4</td>\n",
- " <td>2.1</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>140</th>\n",
- " <td>6.7</td>\n",
- " <td>3.1</td>\n",
- " <td>5.6</td>\n",
- " <td>2.4</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>141</th>\n",
- " <td>6.9</td>\n",
- " <td>3.1</td>\n",
- " <td>5.1</td>\n",
- " <td>2.3</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>142</th>\n",
- " <td>5.8</td>\n",
- " <td>2.7</td>\n",
- " <td>5.1</td>\n",
- " <td>1.9</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>143</th>\n",
- " <td>6.8</td>\n",
- " <td>3.2</td>\n",
- " <td>5.9</td>\n",
- " <td>2.3</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>144</th>\n",
- " <td>6.7</td>\n",
- " <td>3.3</td>\n",
- " <td>5.7</td>\n",
- " <td>2.5</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>145</th>\n",
- " <td>6.7</td>\n",
- " <td>3.0</td>\n",
- " <td>5.2</td>\n",
- " <td>2.3</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>146</th>\n",
- " <td>6.3</td>\n",
- " <td>2.5</td>\n",
- " <td>5.0</td>\n",
- " <td>1.9</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>147</th>\n",
- " <td>6.5</td>\n",
- " <td>3.0</td>\n",
- " <td>5.2</td>\n",
- " <td>2.0</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>148</th>\n",
- " <td>6.2</td>\n",
- " <td>3.4</td>\n",
- " <td>5.4</td>\n",
- " <td>2.3</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>149</th>\n",
- " <td>5.9</td>\n",
- " <td>3.0</td>\n",
- " <td>5.1</td>\n",
- " <td>1.8</td>\n",
- " <td>virginica</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>150 rows × 5 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " sepal_length sepal_width petal_length petal_width species\n",
- "0 5.1 3.5 1.4 0.2 setosa\n",
- "1 4.9 3.0 1.4 0.2 setosa\n",
- "2 4.7 3.2 1.3 0.2 setosa\n",
- "3 4.6 3.1 1.5 0.2 setosa\n",
- "4 5.0 3.6 1.4 0.2 setosa\n",
- "5 5.4 3.9 1.7 0.4 setosa\n",
- "6 4.6 3.4 1.4 0.3 setosa\n",
- "7 5.0 3.4 1.5 0.2 setosa\n",
- "8 4.4 2.9 1.4 0.2 setosa\n",
- "9 4.9 3.1 1.5 0.1 setosa\n",
- "10 5.4 3.7 1.5 0.2 setosa\n",
- "11 4.8 3.4 1.6 0.2 setosa\n",
- "12 4.8 3.0 1.4 0.1 setosa\n",
- "13 4.3 3.0 1.1 0.1 setosa\n",
- "14 5.8 4.0 1.2 0.2 setosa\n",
- "15 5.7 4.4 1.5 0.4 setosa\n",
- "16 5.4 3.9 1.3 0.4 setosa\n",
- "17 5.1 3.5 1.4 0.3 setosa\n",
- "18 5.7 3.8 1.7 0.3 setosa\n",
- "19 5.1 3.8 1.5 0.3 setosa\n",
- "20 5.4 3.4 1.7 0.2 setosa\n",
- "21 5.1 3.7 1.5 0.4 setosa\n",
- "22 4.6 3.6 1.0 0.2 setosa\n",
- "23 5.1 3.3 1.7 0.5 setosa\n",
- "24 4.8 3.4 1.9 0.2 setosa\n",
- "25 5.0 3.0 1.6 0.2 setosa\n",
- "26 5.0 3.4 1.6 0.4 setosa\n",
- "27 5.2 3.5 1.5 0.2 setosa\n",
- "28 5.2 3.4 1.4 0.2 setosa\n",
- "29 4.7 3.2 1.6 0.2 setosa\n",
- ".. ... ... ... ... ...\n",
- "120 6.9 3.2 5.7 2.3 virginica\n",
- "121 5.6 2.8 4.9 2.0 virginica\n",
- "122 7.7 2.8 6.7 2.0 virginica\n",
- "123 6.3 2.7 4.9 1.8 virginica\n",
- "124 6.7 3.3 5.7 2.1 virginica\n",
- "125 7.2 3.2 6.0 1.8 virginica\n",
- "126 6.2 2.8 4.8 1.8 virginica\n",
- "127 6.1 3.0 4.9 1.8 virginica\n",
- "128 6.4 2.8 5.6 2.1 virginica\n",
- "129 7.2 3.0 5.8 1.6 virginica\n",
- "130 7.4 2.8 6.1 1.9 virginica\n",
- "131 7.9 3.8 6.4 2.0 virginica\n",
- "132 6.4 2.8 5.6 2.2 virginica\n",
- "133 6.3 2.8 5.1 1.5 virginica\n",
- "134 6.1 2.6 5.6 1.4 virginica\n",
- "135 7.7 3.0 6.1 2.3 virginica\n",
- "136 6.3 3.4 5.6 2.4 virginica\n",
- "137 6.4 3.1 5.5 1.8 virginica\n",
- "138 6.0 3.0 4.8 1.8 virginica\n",
- "139 6.9 3.1 5.4 2.1 virginica\n",
- "140 6.7 3.1 5.6 2.4 virginica\n",
- "141 6.9 3.1 5.1 2.3 virginica\n",
- "142 5.8 2.7 5.1 1.9 virginica\n",
- "143 6.8 3.2 5.9 2.3 virginica\n",
- "144 6.7 3.3 5.7 2.5 virginica\n",
- "145 6.7 3.0 5.2 2.3 virginica\n",
- "146 6.3 2.5 5.0 1.9 virginica\n",
- "147 6.5 3.0 5.2 2.0 virginica\n",
- "148 6.2 3.4 5.4 2.3 virginica\n",
- "149 5.9 3.0 5.1 1.8 virginica\n",
- "\n",
- "[150 rows x 5 columns]"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import pandas as pd\n",
- "\n",
- "iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')\n",
- "iris"
- ]
- }
- ],
- "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": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/001-Jupyter/004-Create_JupyterKernel_container/install-singularity-jupyter-kernel.ipynb b/001-Jupyter/004-Create_JupyterKernel_container/install-singularity-jupyter-kernel.ipynb
deleted file mode 100644
index d825114d15f1daa158d0ac42120b6b4d74a79645..0000000000000000000000000000000000000000
--- a/001-Jupyter/004-Create_JupyterKernel_container/install-singularity-jupyter-kernel.ipynb
+++ /dev/null
@@ -1,297 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Install containerized Jupyter kernel at Jupyter-JSC\n",
- "\n",
- "This Jupyter notebook will walk you through the installation of a containerized Jupyter kernel (for use at Jupyter-JSC, but it should actually work with any Jupyter server on a system where Singularity is installed). Considerable performance improvements (especially with respect to kernel start-up times) over e.g. conda-based Jupyter kernels on distributed filesystems, as are typically installed on HPC systems, might be experienced. In the example below, the `base-notebook` from the [Jupyter docker stacks](https://jupyter-docker-stacks.readthedocs.io/en/latest/) is used as an IPython kernel (already having the required `ipykernel` package installed), the approach presented here might be extended to any other [Jupyter kernel compatible programming language](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels), though.\n",
- "\n",
- "Requirements:\n",
- "\n",
- "* Python environment with an installed `ipykernel` package in a Docker (or Singularity) container\n",
- "* `container` group access for the JSC systems as described [here](https://apps.fz-juelich.de/jsc/hps/juwels/container-runtime.html#getting-access) in the docs"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Check that the Singularity container runtime is available via the JupyterLab environment,"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "singularity version 3.6.4-1.el8\n"
- ]
- }
- ],
- "source": [
- "singularity --version"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Specify the filesystem location that stores the Singularity container image,"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "IMAGE_TARGET_DIR=/p/project/cesmtst/hoeflich1/jupyter-base-notebook"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Optional, if you already have a Singularity container image available at the above location: Convert a containerized Python environment (e.g. the Jupyter `base-notebook` that is [available via Dockerhub](https://hub.docker.com/r/jupyter/base-notebook)) into a Singularity container image to be used as an example here,"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "mkdir -p ${IMAGE_TARGET_DIR}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Note that pulling and converting the Dockerhub image will take a bit of time,"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "singularity pull ${IMAGE_TARGET_DIR}/jupyter-base-notebook.sif docker://jupyter/base-notebook &> singularity.log"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO: Converting OCI blobs to SIF format\n",
- "INFO: Starting build...\n",
- "Getting image source signatures\n",
- "Copying blob sha256:da7391352a9bb76b292a568c066aa4c3cbae8d494e6a3c68e3c596d34f7c75f8\n",
- "Copying blob sha256:14428a6d4bcdba49a64127900a0691fb00a3f329aced25eb77e3b65646638f8d\n",
- "Copying blob sha256:2c2d948710f21ad82dce71743b1654b45acb5c059cf5c19da491582cef6f2601\n",
- "Copying blob sha256:e3cbfeece0aec396b6793a798ed1b2aed3ef8f8693cc9b3036df537c1f8e34a1\n",
- "Copying blob sha256:48bd2a353bd8ed1ad4b841de108ae42bccecc44b3f05c3fcada8a2a6f5fa09cf\n",
- "Copying blob sha256:235d93b8ccf12e8378784dc15c5bd0cb08ff128d61b856d32026c5a533ac3c89\n",
- "Copying blob sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1\n",
- "Copying blob sha256:b6c06056c45bc1da74604fcf368b02794fe4e36dcae881f4c6b4fa32b37a1385\n",
- "Copying blob sha256:60918bcbe6d44988e4e48db436996106cc7569a4b880488be9cac90ea6883ae0\n",
- "Copying blob sha256:762f9ebe4ddc05e56e33f7aba2cdd1be62f747ecd9c8f9eadcb379debf3ebe06\n",
- "Copying blob sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1\n",
- "Copying blob sha256:1df9d491a0390ecc3f9fac4484c92b2a5f71a79450017f2fca1849f2d6e7f949\n",
- "Copying blob sha256:be84c8c720e3c53037ac2c5cbc53cf9a2a674503b2c995da1351e5560f60cc12\n",
- "Copying blob sha256:28807e96859dc8c00c96255dfa51a0822380638a092803e7143473d1870970fb\n",
- "Copying blob sha256:bcdaf848f29a8bf0efc18a5883dc65a4a7a6b2c6cf4094e5115188ed22165a00\n",
- "Copying blob sha256:49777cff52f155a9ba35e58102ecec7029dddf52aa4947f2cffbd1af12848e81\n",
- "Copying blob sha256:7fb3bffa2e730b052c0c7aabd715303cc5830a05b992f2d3d70afeffa0a9ed4f\n",
- "Copying blob sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1\n",
- "Copying config sha256:79f074439b14ae0634f2f217e5debc159c4e8c3a9ff2e0119e4dc88f9c7e21a5\n",
- "Writing manifest to image destination\n",
- "Storing signatures\n",
- "2021/01/19 11:59:33 info unpack layer: sha256:da7391352a9bb76b292a568c066aa4c3cbae8d494e6a3c68e3c596d34f7c75f8\n",
- "2021/01/19 11:59:34 info unpack layer: sha256:14428a6d4bcdba49a64127900a0691fb00a3f329aced25eb77e3b65646638f8d\n",
- "2021/01/19 11:59:34 info unpack layer: sha256:2c2d948710f21ad82dce71743b1654b45acb5c059cf5c19da491582cef6f2601\n",
- "2021/01/19 11:59:34 info unpack layer: sha256:e3cbfeece0aec396b6793a798ed1b2aed3ef8f8693cc9b3036df537c1f8e34a1\n",
- "2021/01/19 11:59:34 info unpack layer: sha256:48bd2a353bd8ed1ad4b841de108ae42bccecc44b3f05c3fcada8a2a6f5fa09cf\n",
- "2021/01/19 11:59:34 info unpack layer: sha256:235d93b8ccf12e8378784dc15c5bd0cb08ff128d61b856d32026c5a533ac3c89\n",
- "2021/01/19 11:59:34 info unpack layer: sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1\n",
- "2021/01/19 11:59:34 info unpack layer: sha256:b6c06056c45bc1da74604fcf368b02794fe4e36dcae881f4c6b4fa32b37a1385\n",
- "2021/01/19 11:59:34 info unpack layer: sha256:60918bcbe6d44988e4e48db436996106cc7569a4b880488be9cac90ea6883ae0\n",
- "2021/01/19 11:59:34 info unpack layer: sha256:762f9ebe4ddc05e56e33f7aba2cdd1be62f747ecd9c8f9eadcb379debf3ebe06\n",
- "2021/01/19 11:59:34 info unpack layer: sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1\n",
- "2021/01/19 11:59:34 info unpack layer: sha256:1df9d491a0390ecc3f9fac4484c92b2a5f71a79450017f2fca1849f2d6e7f949\n",
- "2021/01/19 11:59:36 info unpack layer: sha256:be84c8c720e3c53037ac2c5cbc53cf9a2a674503b2c995da1351e5560f60cc12\n",
- "2021/01/19 11:59:40 info unpack layer: sha256:28807e96859dc8c00c96255dfa51a0822380638a092803e7143473d1870970fb\n",
- "2021/01/19 11:59:40 info unpack layer: sha256:bcdaf848f29a8bf0efc18a5883dc65a4a7a6b2c6cf4094e5115188ed22165a00\n",
- "2021/01/19 11:59:40 info unpack layer: sha256:49777cff52f155a9ba35e58102ecec7029dddf52aa4947f2cffbd1af12848e81\n",
- "2021/01/19 11:59:40 info unpack layer: sha256:7fb3bffa2e730b052c0c7aabd715303cc5830a05b992f2d3d70afeffa0a9ed4f\n",
- "2021/01/19 11:59:40 info unpack layer: sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1\n",
- "INFO: Creating SIF file...\n"
- ]
- }
- ],
- "source": [
- "cat singularity.log | grep -v warn"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Check that the Singularity image is available,"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "total 177M\n",
- "drwxr-sr-x 2 hoeflich1 cesmtst 4.0K Jan 19 11:59 .\n",
- "drwxr-sr-x 5 hoeflich1 cesmtst 4.0K Jan 19 11:59 ..\n",
- "-rwxr-xr-x 1 hoeflich1 cesmtst 183M Jan 19 11:59 jupyter-base-notebook.sif\n"
- ]
- }
- ],
- "source": [
- "ls -lah ${IMAGE_TARGET_DIR}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now, setup a Jupyter kernel specification with the `install-jupyter-kernel.sh` script from this repository (which basically writes a `kernel.json` file to the home directory location that Jupyter expects for user-specific kernels),"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "KERNEL_DISPLAY_NAME=Singularity-Python # don't use whitespaces here!\n",
- "SINGULARITY_IMAGE=${IMAGE_TARGET_DIR}/jupyter-base-notebook.sif"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "./install-singularity-jupyter-kernel.sh ${KERNEL_DISPLAY_NAME} ${SINGULARITY_IMAGE}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Check that the Jupyter kernel specification was written,"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{\n",
- " \"argv\": [\n",
- " \"singularity\",\n",
- " \"exec\",\n",
- " \"--cleanenv\",\n",
- " \"/p/project/cesmtst/hoeflich1/jupyter-base-notebook/jupyter-base-notebook.sif\",\n",
- " \"python\",\n",
- " \"-m\",\n",
- " \"ipykernel\",\n",
- " \"-f\",\n",
- " \"{connection_file}\"\n",
- " ],\n",
- " \"language\": \"python\",\n",
- " \"display_name\": \"Singularity-Python\"\n",
- "}\n"
- ]
- }
- ],
- "source": [
- "cat ${HOME}/.local/share/jupyter/kernels/${KERNEL_DISPLAY_NAME}/kernel.json"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "And that the above Singularity-Python kernel is visible by the Jupyter server,"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Available kernels:\n",
- " singularity-python /p/home/jusers/hoeflich1/juwels/.local/share/jupyter/kernels/Singularity-Python\n",
- " ruby /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-Ruby/2.6.3-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/ruby\n",
- " ir35 /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-R/3.5.3-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/ir35\n",
- " pyquantum-1.0 /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-PyQuantum/1.0-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/pyquantum-1.0\n",
- " pyparaview-5.8 /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-PyParaView/5.8.0-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/pyparaview-5.8\n",
- " octave /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-Octave/5.1.0-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/octave\n",
- " julia-1.4 /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-Julia/1.4.2-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/julia-1.4\n",
- " javascript /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-JavaScript/5.2.0-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/javascript\n",
- " cling-cpp17 /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-Cling/0.6-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/cling-cpp17\n",
- " bash /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-Bash/0.7.1-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/bash\n",
- " python3 /p/software/juwels/stages/Devel-2019a/software/Jupyter/2019a.2.4-gcccoremkl-8.3.0-2019.3.199-Python-3.6.8/share/jupyter/kernels/python3\n"
- ]
- }
- ],
- "source": [
- "jupyter kernelspec list"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If so, you should be able to choose and connect to the containerized Python kernel from the drop down menu and/or the kernel launcher tab (a reload of the JupyterLab web page might be necessary)."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Bash",
- "language": "bash",
- "name": "bash"
- },
- "language_info": {
- "codemirror_mode": "shell",
- "file_extension": ".sh",
- "mimetype": "text/x-sh",
- "name": "bash"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/001-Jupyter/004-Create_JupyterKernel_container/setup-singularity-jupyter-server.ipynb b/001-Jupyter/004-Create_JupyterKernel_container/setup-singularity-jupyter-server.ipynb
deleted file mode 100644
index 4301aa46e38bbe7a47645040cb4e04c17661289c..0000000000000000000000000000000000000000
--- a/001-Jupyter/004-Create_JupyterKernel_container/setup-singularity-jupyter-server.ipynb
+++ /dev/null
@@ -1,173 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Setup containerized Jupyter server for Jupyter-JSC\n",
- "\n",
- "This Jupyter notebook will explain how to setup a containerized Jupyter server at Jupyter-JSC. It makes use of the expert features described on page 22 (as of November 25th, 2020) of the training material available [here](https://jupyter-jsc.fz-juelich.de/nbviewer/github/FZJ-JSC/jupyter-jsc-notebooks/blob/master/Jupyter-JSC_supercomputing-in-the-browser.pdf). Please note, that setting up a containerized Jupyter server for the JupyterHub at JSC might introduce certain drawbacks to your Jupyter-JSC experience. Specifically, you will be restricted to the software environment that is installed in your container environment only, which might introduce unwanted side-effects to your JupyterLab-based workflows on the JSC HPC systems. For example, usage of the SLURM batch scheduler commands is not possible, because the SLURM libraries are not visible from the container environment per default. Also, you won't be able to use the Lmod software environment modules provided by JSC. Please note, that if these kind of side-effects are not acceptable, you might rather use a containerized Jupyter kernel as [described here](install-singularity-jupyter-kernel.ipynb). You could also setup your own non-containerized JupyterLab server.\n",
- "\n",
- "Please note, you can switch back to the default Jupyter-JSC server environment anytime by deleting `$HOME/.jupyter/start_jupyter-jsc.sh` after [login to the JSC systems](https://apps.fz-juelich.de/jsc/hps/juwels/access.html) via SSH.\n",
- "\n",
- "Requirements:\n",
- "\n",
- "* Jupyter server environment in a Docker (or Singularity) container\n",
- "* `container` group access for the JSC systems as described [here](https://apps.fz-juelich.de/jsc/hps/juwels/container-runtime.html#getting-access) in the docs\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Specify the filesystem location that stores the Singularity container image,"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "IMAGE_TARGET_DIR=/p/project/cesmtst/hoeflich1/jupyter-base-notebook"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Convert the example Jupyter base-notebook (that is [available via Dockerhub](https://hub.docker.com/r/jupyter/base-notebook)) into a Singularity container image,"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "mkdir -p ${IMAGE_TARGET_DIR}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "singularity pull --force ${IMAGE_TARGET_DIR}/jupyter-base-notebook.sif docker://jupyter/base-notebook &> singularity.log"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO: Using cached SIF image\n"
- ]
- }
- ],
- "source": [
- "cat singularity.log | grep -v warn"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Check that the Singularity image is available,"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "total 177M\n",
- "drwxr-sr-x 2 hoeflich1 cesmtst 4.0K Jan 19 18:50 .\n",
- "drwxr-sr-x 5 hoeflich1 cesmtst 4.0K Jan 19 18:05 ..\n",
- "-rwxr-xr-x 1 hoeflich1 cesmtst 183M Jan 19 18:50 jupyter-base-notebook.sif\n"
- ]
- }
- ],
- "source": [
- "ls -lah ${IMAGE_TARGET_DIR}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now, manually (!) specify the Singularity image filesystem location in the `start_jupyter-jsc.sh` script and check that the specified path is correct,"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "#!/bin/bash\n",
- "\n",
- "# Author: Katharina Höflich\n",
- "# Repository: https://github.com/FZJ-JSC/jupyter-jsc-notebooks\n",
- "\n",
- "SINGULARITY_IMAGE=/p/project/cesmtst/hoeflich1/jupyter-base-notebook/jupyter-base-notebook.sif\n",
- "JUPYTERJSC_USER_CMD=\"singularity exec ${SINGULARITY_IMAGE} jupyterhub-singleuser --config ${JUPYTER_LOG_DIR}/.config.py\"\n"
- ]
- }
- ],
- "source": [
- "cat start_jupyter-jsc.sh"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "And copy the `start_jupyter-jsc.sh` script to the filesystem location expected by Jupyter-JSC,"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "cp start_jupyter-jsc.sh $HOME/.jupyter/start_jupyter-jsc.sh"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Finally, opening a new Jupyter session via the Jupyter-JSC control panel should now load the containerized Jupyter server that was setup here."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Bash",
- "language": "bash",
- "name": "bash"
- },
- "language_info": {
- "codemirror_mode": "shell",
- "file_extension": ".sh",
- "mimetype": "text/x-sh",
- "name": "bash"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/001-Jupyter/Activate_JupyterJSC_2-factor-authentication.ipynb b/001-Jupyter/Activate_JupyterJSC_2-factor-authentication.ipynb
deleted file mode 100644
index f1f336dac485895f25d6bb9022e38eaa50944231..0000000000000000000000000000000000000000
--- a/001-Jupyter/Activate_JupyterJSC_2-factor-authentication.ipynb
+++ /dev/null
@@ -1,236 +0,0 @@
-{
- "cells": [
- {
- "attachments": {
- "67258d94-84e6-4a0c-ae8f-c74332ec082e.jpg": {
- "image/jpeg": 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"
- }
- },
- "cell_type": "markdown",
- "metadata": {
- "toc-hr-collapsed": false
- },
- "source": [
- "\n",
- "Author: [Jens Henrik Göbbert](mailto:j.goebbert@fz-juelich.de)\n",
- "------------------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 2-Factor Authentication (2FA)\n",
- "<div>\n",
- " <img src=https://jupyter-jsc.fz-juelich.de/hub/static/images/2fa/jupyter-jsc_2fa_img01.png title=\"2-factor-authentication\" width=\"320\" style=\"float:left\"/>\n",
- " <!-- <img src=images/jupyter-jsc_2fa_img01.png title=\"2-factor-authentication\" width=\"320\" style=\"float:left\"/> -->\n",
- "</div>\n",
- "\n",
- "## Introduction\n",
- "2-Factor Authentication (2FA), sometimes referred to as two-factor verification, is a security method in which you provide **two different authentication factors** to identify yourself at login.\n",
- "This process is **performed to better protect** both your credentials and the resources that you can access.\n",
- "\n",
- "In the **first login step**, you start with the usual entry of a good password. The service then confirms the correctness of the password entered.\n",
- "This does not, however, lead directly to the desired entrance - but to a further barrier.\n",
- "\n",
- "The **second login step** prevents unauthorized third parties from gaining access to your account just because they might have stolen your password.\n",
- "A quite common 2nd-factor is a **One-Time Password (OTP)** generated by a so-called **OTP-App** you install and initialize once on one of your personal devices.\n",
- "This *OTP-app* then provides (in our case every 30 seconds) a new *one-time password* that needs to be entered on the login page.\n",
- " \n",
- "<div style=\"clear:both\"></div>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- " <video controls src=\"https://multimedia.gsb.bund.de/BSI/Video/2-Faktor-Authentisierung_SD.conv.mp4\" width=480 style=\"float:right\"/>\n",
- "</div>\n",
- "\n",
- "## Basic Principle\n",
- "These two factors for authentication combine the building blocks **knowledge** and **possession** in the login procedure. \n",
- "- **knowledge** - the secret knowledge is the password you enter. \n",
- "- **possession** - With the *one-time password* you show that you are in possession of a certain device (e.g. your smartphone), because only the *OTP-App*, installed on that device, can generate it. \n",
- "\n",
- "<div style=\"clear:both\"></div>\n",
- "<div>\n",
- " <p style=\"float:right\">Source: Bundesamt für Sicherheit in der Informationstechnik</p>\n",
- "</div>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- " <img src=https://jupyter-jsc.fz-juelich.de/hub/static/images/2fa/jupyter-jsc_2fa_img02.png title=\"2-factor-authentication\" width=\"320\" style=\"float:left\"/>\n",
- " <!-- <img src=images/jupyter-jsc_2fa_img02.png title=\"2-factor-authentication\" width=\"320\" style=\"float:left\"/> -->\n",
- "</div>\n",
- "\n",
- "## Algorithm\n",
- "The **OTP-App** can calculate personal one-time passwords completely autonomously from the outside world using a standardized and open algorithm for the generation of **Time-based One-Time Passwords (TOTP)**. \n",
- "\n",
- "The *TOTP algorithm* was published in 2011 by the [Internet Engineering Task Force (IETF)](https://www.ietf.com) as [RFC 6238](https://tools.ietf.org/html/rfc6238). The *TOTP algorithm* is a hash function in which a secret code is hashed together with the current time.\n",
- "Behind the hash function is the HMAC-based One-time Password Algorithm according to [RFC 4226](https://tools.ietf.org/html/rfc4226) - in simple terms nothing more than a standard that forms a hash in a certain way.\n",
- "\n",
- "The calculation includes both a **\"secret initialization code\"**, that is known to both the server and the client, and the **current time**.\n",
- "The final *one-time password* is generated from these two inputs and is valid for a certain period of time. (in our case for **30 seconds**).\n",
- "The procedure can be implemented in such a way that slight differences in time between client and server are accepted.\n",
- "\n",
- "Hence, any *one-time password* is time-based, calculated locally, and always unique.\n",
- "\n",
- "<div style=\"clear:both\"></div>\n",
- "\n",
- "------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# How to get started with 2FA\n",
- "<div>\n",
- " <img src=https://jupyter-jsc.fz-juelich.de/hub/static/images/2fa/jupyter-jsc_2fa_img03.png title=\"2-factor-authentication\" width=\"320\" style=\"float:right\"/>\n",
- " <!-- <img src=images/jupyter-jsc_2fa_img03.png title=\"2-factor-authentication\" width=\"320\" style=\"float:right\"/> -->\n",
- "</div>\n",
- "\n",
- "## Preparation\n",
- "\n",
- "To get ready to use 2-Factor Authentication (2FA) for Jupyter-JSC you have to **prepare** it ONCE: \n",
- "- (1) **request 2FA** for Jupyter-JSC, \n",
- " - (a) login to [Jupyter-JSC](https://jupyter-jsc.fz-juelich.de) \n",
- " - (b) visit https://jupyter-jsc.fz-juelich.de/2fa and request 2FA \n",
- " - (c) wait for a *confirmation emails* and click the provided *activation link* \n",
- "- (2) **activate 2FA** for Juypter-JSC,\n",
- " - (a) install an **OTP-App**, which supports the TOTP algorithm \n",
- " - (b) communicate the **secret initialization code** to this *OTP-App* \n",
- " - (c) test a first **one-time password** generated. \n",
- "\n",
- "... and then 2FA is ready to be used next time you log in.\n",
- "\n",
- "### 1. Request 2FA\n",
- "Please login to Jupyter-JSC as usual through https://jupyter-jsc.fz-juelich.de \n",
- "and visit the webpage **https://jupyter-jsc.fz-juelich.de/2fa** for requesting 2FA.\n",
- "\n",
- "Please read the notes on this webpage carefully and click the button **Request 2FA** to start. \n",
- "A **confirmation email** including an **activation link** will be send to you directly.\n",
- "\n",
- "### 2. Activate 2FA\n",
- "Please follow this *activation link* to instruct Jupyter-JSC for preparation of your 2FA. \n",
- "You will be asked to re-login to your account to recieve a **secret initialization code** as QR-Code (and string) \n",
- "for a required *OTP-App*. \n",
- "\n",
- "So first, you need to install an **OTP-App** on one of your personal devices (if you haven´t done so already), \n",
- "which you plan to use in the future to generate the required **one-time passwords** for each time you log in:\n",
- "\n",
- "<div style=\"clear:both\"></div>\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- " <img src=https://jupyter-jsc.fz-juelich.de/hub/static/images/2fa/jupyter-jsc_2fa_img04.png title=\"2-factor-authentication\" width=\"320\" style=\"float:left\"/>\n",
- " <!-- <img src=images/jupyter-jsc_2fa_img04.png title=\"2-factor-authentication\" width=\"320\" style=\"float:left\"/> -->\n",
- "</div>\n",
- "<div>\n",
- " <!-- <img src=https://jupyter-jsc.fz-juelich.de/hub/static/images/2fa/jupyter-jsc_2fa_img04-1.png title=\"2-factor-authentication\" width=\"320\" style=\"float:right\"/>-->\n",
- " <img src=https://raw.githubusercontent.com/FZJ-JSC/jupyter-jsc-notebooks/master/001-Jupyter/images/jupyter-jsc_2fa_img04-1.png title=\"2-factor-authentication\" width=\"120\" style=\"float:right\"/>\n",
- "</div>\n",
- "\n",
- "### a. OTP-App Installation\n",
- "There are a large number of different *OTP-Apps* available that implemented the *TOTP algorithm*. \n",
- "You have to install **one of them** - for example, take one of the following: \n",
- "\n",
- "Recommended, free & open-source:\n",
- " - [**FreeOTP**](https://freeotp.github.io) ([iOS](https://apps.apple.com/de/app/freeotp-authenticator/id872559395), [Android](https://play.google.com/store/apps/details?id=org.fedorahosted.freeotp&hl=de)) \n",
- " - [**KeeWeb**](https://keeweb.info) ([Windows](https://keeweb.info), [macOS](https://keeweb.info), [Linux](https://keeweb.info), [online](https://keeweb.info))\n",
- "\n",
- "Free, but closed source:\n",
- " - [**Authy**](https://authy.com/download/) ([iOS](https://apps.apple.com/de/app/authy/id494168017), [Android](https://play.google.com/store/apps/details?id=com.authy.authy), [Windows](https://authy.com/download/), [macOS](https://authy.com/download/), [Linux](https://snapcraft.io/authy)) \n",
- " - [**Protectimus Smart OTP**](https://www.protectimus.com/protectimus-smart) ([iOS](https://apps.apple.com/ie/app/protectimus-smart/id854508919), [Android](https://play.google.com/store/apps/details?id=com.protectimus.android)) \n",
- " - [**Google Authenticator**](https://de.wikipedia.org/wiki/Google_Authenticator) ([iOS](https://apps.apple.com/de/app/google-authenticator/id388497605), [Android](https://play.google.com/store/apps/details?id=com.google.android.apps.authenticator2) ) \n",
- " - [**Microsoft Authenticator**](https://www.microsoft.com/en-us/account/authenticator) ([iOS](https://apps.apple.com/de/app/microsoft-authenticator/id983156458), [Android](https://play.google.com/store/apps/details?id=com.azure.authenticator), [Windows 10 Mobile](https://www.microsoft.com/en-us/p/microsoft-authenticator/9nblgggzmcj6)) \n",
- "\n",
- "The *TOTP algorithm* can also be implemented in hardware as a so-called \"hardware token\" (e.g. [Protectimus Tokens](https://www.protectimus.com/tokens/), [Microcosm Tokens](https://www.microcosm.com/products/oath-otp-authentication-tokens)) \n",
- " \n",
- "<div style=\"clear:both\"></div>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- " <img src=https://jupyter-jsc.fz-juelich.de/hub/static/images/2fa/jupyter-jsc_2fa_img05.png title=\"2-factor-authentication\" width=\"320\" style=\"float:left\"/>\n",
- " <!-- <img src=images/jupyter-jsc_2fa_img05.png title=\"2-factor-authentication\" width=\"320\" style=\"float:left\"/> -->\n",
- "</div>\n",
- "\n",
- "### b. OTP-App Initialization & Validation\n",
- "Before you can use 2FA for Jupyter-JSC a random, user-specific, unique and **secret initialization code** must be known by both Jupyter-JSC and the your *OTP-App*.\n",
- "This *secret initialization code* gets generated by Jupyter-JSC and is shown as a **QR-Code** (or string) on the activation page.\n",
- "\n",
- "The QR-Code provides the *secret initialization code* with the descriptive data (1) algorithm = TOTP, (2) period of validity = 30s.\n",
- "**If you prefer to use the string** instead of the QR-Code, please ensure you set these descriptive dates manually in your *OTP-App*.\n",
- "\n",
- "Next, the *OTP-App* provides now a **verification code** you have to enter on the activation webpage.\n",
- "Jupyter-JSC compares the *verification code* you provide with the one generated by Jupyter-JSC.\n",
- "\n",
- "If they match, **2FA is now activated**.\n",
- "\n",
- "<div style=\"clear:both\"></div>\n",
- "\n",
- "----------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- " <img src=https://jupyter-jsc.fz-juelich.de/hub/static/images/2fa/jupyter-jsc_2fa_img06.png title=\"2-factor-authentication\" width=\"320\" style=\"float:right\"/>\n",
- " <!-- <img src=images/jupyter-jsc_2fa_img06.png title=\"2-factor-authentication\" width=\"320\" style=\"float:right\"/> -->\n",
- "</div>\n",
- "\n",
- "### 2FA-Login at Jupyter-JSC\n",
- "Congratulation! You are now ready to use 2-Factor Authentication with Jupyter-JSC.\n",
- "\n",
- "Login is now as simple as this\n",
- "1. **Enter your JSC-account password** \n",
- " Each time you log in, you enter your JSC-account password as usual. \n",
- "2. **Enter the current one-time password** \n",
- " You will then be asked for a *one-time password* that you can read from your installed & initialized *OTP-App* (e.g. on your smartphone). \n",
- " \n",
- "**Remember me** \n",
- "Jupyter-JSC can set a cookie to remember, that you have logged in from this device already. \n",
- "Just check the \"Remember me\" **checkbox** where you enter *one-time password* . \n",
- "Jupyter-JSC **skips the request** of a *one-time password* in this browser on that device then for **one week**. \n",
- " \n",
- " "
- ]
- }
- ],
- "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": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/001-Jupyter/Create_JupyterKernel_conda.ipynb b/001-Jupyter/Create_JupyterKernel_conda.ipynb
deleted file mode 100644
index 111850ec1cda5cc7ca6f5b1461c74dcc3c8dffb1..0000000000000000000000000000000000000000
--- a/001-Jupyter/Create_JupyterKernel_conda.ipynb
+++ /dev/null
@@ -1,319 +0,0 @@
-{
- "cells": [
- {
- "attachments": {
- "500ad0ee-8923-43bb-9336-e838a26dc2f1.jpg": {
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"
- }
- },
- "cell_type": "markdown",
- "metadata": {
- "toc-hr-collapsed": false
- },
- "source": [
- " \n",
- "Author: [Sebastian Lührs](mailto:s.luehrs@fz-juelich.de)\n",
- "--------------------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Create your own Jupyter CONDA-Kernel\n",
- "\n",
- "Often the standard kernel do not provide all features you need for your work. This might be that certain modules are not loaded or packages are not installed.\n",
- "With your own kernel you can overcome that problem easily and define your own environment, in which you work.\n",
- "\n",
- "This notebook shows you how you can build your own kernel for a **conda environment**.\n",
- "\n",
- "--------------------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Building your own Jupyter CONDA-kernel is a three step process\n",
- "Download Minconda installer\n",
- "1. Download/Install Miniconda\n",
- " * Miniconda3.sh\n",
- "2. Create Conda Environment\n",
- " * conda create\n",
- "2. Create/Edit launch script for the Jupyter kernel\n",
- " * kernel.sh\n",
- "3. Create/Edit Jupyter kernel configuration\n",
- " * kernel.json"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Settings"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Selectable **CONDA_TARGET_DIR** path for the central conda installation, should be in the project filesystem"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "export CONDA_TARGET_DIR=${HOME}/PROJECT_training2005/testdir/miniconda3"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Selectable **CONDA_ENV** name, will be used to specify the environment name\n",
- " - must be lowercase"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "CONDA_ENV=my_env\n",
- "\n",
- "export CONDA_ENV=$(echo \"${CONDA_ENV}\" | awk '{print tolower($0)}')\n",
- "echo ${CONDA_ENV} # double check"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "## 1. Download/Install Miniconda"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Start here if you want to run the full installation.\n",
- "If you want to create another environment in an existing conda setup go to **create environment**. If you want to attach yourself to an existing environment go to **create user kernel**."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 1.1 - Download Minconda installer"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "wget --output-document=$HOME/Miniconda3.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 1.2 - Create target directory"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mkdir -p ${CONDA_TARGET_DIR}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 1.3 - Install Miniconda"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "bash $HOME/Miniconda3.sh -b -u -p ${CONDA_TARGET_DIR}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "${CONDA_TARGET_DIR}/bin/conda init bash"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 1.4 - Disable automatic activation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "${CONDA_TARGET_DIR}/bin/conda config --set auto_activate_base false"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "## 2. Create conda environment"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Create new conda environment. The following steps can be repeated if multiple environments should be created. If the Python version differ towards the external Python version, a mix of Conda modules and external modules will not be possible"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "${CONDA_TARGET_DIR}/bin/conda create -n ${CONDA_ENV} -y python=3.6.8 ipykernel"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "## 3. Create/Edit launch script for the Jupyter kernel"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 3.1 - Create kernel to allow access to the conda environment. Adapte `module purge` and `PYTHONPATH` according to the comments."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "echo '#!/bin/bash\n",
- "\n",
- "# module purge # optional to disable the external environment, necessary, if python version is different\n",
- " \n",
- "# Activate your Python virtual environment\n",
- "source '\"${CONDA_TARGET_DIR}\"'/bin/activate '\"${CONDA_ENV}\"'\n",
- " \n",
- "# Ensure python packages installed in conda are always prefered, not necessary if module purge is used\n",
- "export PYTHONPATH=${CONDA_PREFIX}/lib/python3.6/site-packages:${PYTHONPATH}\n",
- " \n",
- "exec python -m ipykernel $@' > ${CONDA_TARGET_DIR}/envs/${CONDA_ENV}/kernel.sh"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "chmod +x ${CONDA_TARGET_DIR}/envs/${CONDA_ENV}/kernel.sh"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "## 4. Create/Edit Jupyter kernel configuration"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 4.1 - Create user kernel, if you want to access the conda environment of a colleague, only these steps are necessary"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mkdir -p $HOME/.local/share/jupyter/kernels/conda_${CONDA_ENV}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 4.2 - Adjust kernel.json file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "echo '{\n",
- " \"argv\": [\n",
- " \"'\"${CONDA_TARGET_DIR}\"'/envs/'\"${CONDA_ENV}\"'/kernel.sh\",\n",
- " \"-f\",\n",
- " \"{connection_file}\"\n",
- " ],\n",
- " \"display_name\": \"conda_'\"${CONDA_ENV}\"'\",\n",
- " \"language\": \"python\"\n",
- "}' > $HOME/.local/share/jupyter/kernels/conda_${CONDA_ENV}/kernel.json"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Restart of JupyterLab might be necessary to see the kernel in the kernel selection overview."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Bash",
- "language": "bash",
- "name": "bash"
- },
- "language_info": {
- "codemirror_mode": "shell",
- "file_extension": ".sh",
- "mimetype": "text/x-sh",
- "name": "bash"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/001-Jupyter/Create_JupyterKernel_general.ipynb b/001-Jupyter/Create_JupyterKernel_general.ipynb
deleted file mode 100644
index 43a8eb23edac0a193d8ec32d9b0cea03070d0a98..0000000000000000000000000000000000000000
--- a/001-Jupyter/Create_JupyterKernel_general.ipynb
+++ /dev/null
@@ -1,470 +0,0 @@
-{
- "cells": [
- {
- "attachments": {
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"
- }
- },
- "cell_type": "markdown",
- "metadata": {
- "toc-hr-collapsed": false
- },
- "source": [
- "\n",
- "Author: [Jens Henrik Göbbert](mailto:j.goebbert@fz-juelich.de)\n",
- "------------------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "toc-hr-collapsed": false
- },
- "source": [
- "# Create your own Jupyter Kernel\n",
- "\n",
- "Often the standard kernel do not provide all features you need for your work. This might be that certain modules are not loaded or packages are not installed. \n",
- "With your own kernel you can overcome that problem easily and define your own environment, in which you work.\n",
- "\n",
- "This notebook shows you how you can build your own kernel for a **python environment**.\n",
- "\n",
- "-------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Building your own Jupyter kernel is a three step process\n",
- "1. Create/Pimp new virtual Python environment\n",
- " * venv\n",
- "2. Create/Edit launch script for the Jupyter kernel\n",
- " * kernel.sh\n",
- "3. Create/Edit Jupyter kernel configuration\n",
- " * kernel.json"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Settings"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* Set kernel name\n",
- " - must be lower case\n",
- " - change if you like"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# INPUT NEEDED:\n",
- "KERNEL_NAME=${USER}_kernel\n",
- "\n",
- "export KERNEL_NAME=$(echo \"${KERNEL_NAME}\" | awk '{print tolower($0)}')\n",
- "echo ${KERNEL_NAME} # double check"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* List directories where JupyterLab will search for kernels"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# JUPYTER SEARCH PATH (for kernels-directory)\n",
- "echo \"jupyter search paths for kernels-directories\"\n",
- "if [ -z $JUPYTER_PATH ]; then\n",
- " echo \"$HOME/.local/share/jupyter\"\n",
- "else\n",
- " tr ':' '\\n' <<< \"$JUPYTER_PATH\"\n",
- "fi"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div class=\"alert alert-block alert-info\">\n",
- "<b>Attention:</b>\n",
- "Please choose 'private kernel' if you are unsure.</br>\n",
- "Using 'project kernel's need to be enabled for your project first by our Jupyter-JSC admins.\n",
- "</div>\n",
- "\n",
- "* Set kernel type\n",
- " - private kernel = \"\\${HOME}/.local/\" \n",
- " - project kernel = \"\\${PROJECT}/.local/\" \n",
- " - other kernel = \"\\<your-path\\>\" (ensure it is part of $JUPYTER_PATH or your kernel will not be found by JuypterLab)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# INPUT NEEDED:\n",
- "export KERNEL_TYPE=private # private, project or other\n",
- "export KERNEL_SPECS_PREFIX=${HOME}/.local\n",
- "\n",
- "###################\n",
- "# project kernel\n",
- "if [ \"${KERNEL_TYPE}\" == \"project\" ]; then\n",
- " export KERNEL_SPECS_PREFIX=${PROJECT}/.local\n",
- " echo \"project kernel\"\n",
- "# private kernel\n",
- "elif [ \"${KERNEL_TYPE}\" == \"private\" ]; then\n",
- " export KERNEL_SPECS_PREFIX=${HOME}/.local\n",
- " echo \"private kernel\"\n",
- "else\n",
- " if [ ! -d \"$KERNEL_SPECS_PREFIX\" ]; then\n",
- " echo \"ERROR: please create directory $KERNEL_SPECS_PREFIX\"\n",
- " fi\n",
- " echo \"other kernel\"\n",
- "fi\n",
- "export KERNEL_SPECS_DIR=${KERNEL_SPECS_PREFIX}/share/jupyter/kernels\n",
- "\n",
- "# check if kernel name is unique\n",
- "if [ -d \"${KERNEL_SPECS_DIR}/${KERNEL_NAME}\" ]; then\n",
- " echo \"ERROR: Kernel already exists in ${KERNEL_SPECS_DIR}/${KERNEL_NAME}\"\n",
- " echo \" Rename kernel name or remove directory.\"\n",
- "fi\n",
- "\n",
- "echo ${KERNEL_SPECS_DIR}/${KERNEL_NAME} # double check"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* Set directory for kernels virtual environment\n",
- " - change if you like"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# INPUT NEEDED:\n",
- "export KERNEL_VENVS_DIR=${PROJECT}/${USER}/jupyter/kernels\n",
- "\n",
- "###################\n",
- "mkdir -p ${KERNEL_VENVS_DIR}\n",
- "if [ \"${KERNEL_TYPE}\" != \"private\" ] && [ \"${KERNEL_TYPE}\" != \"other\" ]; then\n",
- " echo \"Please check the permissions and ensure your project partners have read/execute permissions:\"\n",
- " namei -l ${KERNEL_VENVS_DIR}\n",
- "fi\n",
- "\n",
- "echo ${KERNEL_VENVS_DIR} # double check\n",
- "ls -lt ${KERNEL_VENVS_DIR}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 1. Create/Pimp new virtual Python environment"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 1.1 - Load basic Python module"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "module -q purge\n",
- "module -q use $OTHERSTAGES \n",
- "module -q load Stages/2020 2> /dev/null # any stage can be used\n",
- "module -q load GCCcore/.9.3.0 2> /dev/null\n",
- "module -q load Python/3.8.5 # only Python is required\n",
- "module list # double check"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 1.2 - Load extra modules you need for your kernel"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# module load <module you need>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 1.3 - Create and activate a virtual environment for the kernel \n",
- "and ensure python packages installed in the virtual environment are always prefered"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "if [ -d \"${KERNEL_VENVS_DIR}/${KERNEL_NAME}\" ]; then\n",
- " echo \"ERROR: Directory for virtual environment already ${KERNEL_VENVS_DIR}/${KERNEL_NAME}\"\n",
- " echo \" Rename kernel name or remove directory.\"\n",
- "else\n",
- " python -m venv --system-site-packages ${KERNEL_VENVS_DIR}/${KERNEL_NAME}\n",
- " source ${KERNEL_VENVS_DIR}/${KERNEL_NAME}/bin/activate\n",
- " export PYTHONPATH=${VIRTUAL_ENV}/lib/python3.8/site-packages:${PYTHONPATH}\n",
- " echo ${VIRTUAL_ENV} # double check\n",
- "fi"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 1.4 - Install Python libraries required for communication with Jupyter"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "which pip\n",
- "if [ -z \"${VIRTUAL_ENV}\" ]; then\n",
- " echo \"ERROR: Virtual environment not successfully initialized.\"\n",
- "else\n",
- " pip install --ignore-installed ipykernel\n",
- " ls ${VIRTUAL_ENV}/lib/python3.8/site-packages/ # double check\n",
- "fi"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 1.5 - Install whatever else you need in your Python virtual environment (using pip)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#pip install <python-package you need>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2. Create/Edit launch script for the Jupyter kernel"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 2.1 - Create launch script, which loads your Python virtual environment and starts the ipykernel process inside:\n",
- "\n",
- "<div class=\"alert alert-block alert-info\">\n",
- "<b>Attention:</b>\n",
- "You MUST load the exactly the same modules as you did above for your virtual Python environment.\n",
- "</div>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "echo '#!/bin/bash'\"\n",
- "\n",
- "# Load basic Python module\n",
- "module purge\n",
- "module use \"'$OTHERSTAGES'\"\n",
- "module load Stages/2020\n",
- "module load GCCcore/.9.3.0\n",
- "module load Python/3.8.5\n",
- "\n",
- "# Load extra modules you need for your kernel (as you did in step 1.2)\n",
- "#module load <module you need>\n",
- " \n",
- "# Activate your Python virtual environment\n",
- "source ${KERNEL_VENVS_DIR}/${KERNEL_NAME}/bin/activate\n",
- " \n",
- "# Ensure python packages installed in the virtual environment are always prefered\n",
- "export PYTHONPATH=${VIRTUAL_ENV}/lib/python3.8/site-packages:\"'${PYTHONPATH}'\"\n",
- " \n",
- "exec python -m ipykernel \"'$@' > ${VIRTUAL_ENV}/kernel.sh\n",
- "chmod +x ${VIRTUAL_ENV}/kernel.sh\n",
- "\n",
- "cat ${VIRTUAL_ENV}/kernel.sh # double check"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 3. Create/Edit Jupyter kernel configuration"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 3.1 - Create Jupyter kernel configuration directory and files"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "python -m ipykernel install --name=${KERNEL_NAME} --prefix ${VIRTUAL_ENV}\n",
- "export VIRTUAL_ENV_KERNELS=${VIRTUAL_ENV}/share/jupyter/kernels"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 3.2 - Adjust kernel.json file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mv ${VIRTUAL_ENV_KERNELS}/${KERNEL_NAME}/kernel.json ${VIRTUAL_ENV_KERNELS}/${KERNEL_NAME}/kernel.json.orig\n",
- "\n",
- "echo '{\n",
- " \"argv\": [\n",
- " \"'${KERNEL_VENVS_DIR}/${KERNEL_NAME}/kernel.sh'\",\n",
- " \"-m\",\n",
- " \"ipykernel_launcher\",\n",
- " \"-f\",\n",
- " \"{connection_file}\"\n",
- " ],\n",
- " \"display_name\": \"'${KERNEL_NAME}'\",\n",
- " \"language\": \"python\"\n",
- "}' > ${VIRTUAL_ENV_KERNELS}/${KERNEL_NAME}/kernel.json\n",
- "\n",
- "cat ${VIRTUAL_ENV_KERNELS}/${KERNEL_NAME}/kernel.json # double check"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 3.3 - Create link to kernel specs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mkdir -p ${KERNEL_SPECS_DIR}\n",
- "cd ${KERNEL_SPECS_DIR}\n",
- "ln -s ${VIRTUAL_ENV_KERNELS}/${KERNEL_NAME} .\n",
- "\n",
- "echo -e \"\\n\\nThe new kernel '${KERNEL_NAME}' was added to your kernels in '${KERNEL_SPECS_DIR}/'\\n\"\n",
- "ls ${KERNEL_SPECS_DIR} # double check"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 4. Cleanup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "deactivate"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Bash",
- "language": "bash",
- "name": "bash"
- },
- "language_info": {
- "codemirror_mode": "shell",
- "file_extension": ".sh",
- "mimetype": "text/x-sh",
- "name": "bash"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/001-Jupyter/Create_JupyterKernel_pyenv.ipynb b/001-Jupyter/Create_JupyterKernel_pyenv.ipynb
deleted file mode 100644
index b1aee1885ffb17bfccd96d541c58234b67e0718b..0000000000000000000000000000000000000000
--- a/001-Jupyter/Create_JupyterKernel_pyenv.ipynb
+++ /dev/null
@@ -1,497 +0,0 @@
-{
- "cells": [
- {
- "attachments": {
- "500ad0ee-8923-43bb-9336-e838a26dc2f1.jpg": {
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B/ZLMQqjiSe6m2ds982/82YfT8h5dMUci6biX5WTyJ7vZ0B7meO3QnGqVgoreW80c8fLXG2ofsB43WRDyZTkfO7X9ev8ATXo6t1hN9nTjz8M8s+VPC0/yiHSwCscH2VMElVjCdMgH0TjNJruMawGU6W455VHqnxvF1r2W5cvCoXa4XTMMxlctqH5UJYm1IeRxj5vrEGz+vW4Hb0SYZPyxxFfIbOjQ+LyFv7Vpurg7r/CTsp7OmPaW0o9JXtQ27c/xN/bp2OsQiaQ3fATDs/w351s62nuYtlxSiQzT22FXUOQGrl/tWwEN01r1iCWWbQgy+kpjnyzn31uXvC8bT7pY4ypA7PJk9IeOrlUe92l+8PNMj7P0Z0hc4815Dieea2Hi5d4Lt5o2gIGlcamGPZWzLZroG5a+mjmi4atI14BHsqC6N67q+02tDAQNGjJ99Nbi7dTFfAaN6iJuhg40+kTRgkvDIjzMixxlSAMcmX0lP2uVXr2+02uoYUcR28joWlccyABnSOVTM9zvc2ZmcGRWMcnkAOyOfA+FbPjmvpZhf7P3zEhew/Z4rw+1UXVb9us9ZSIlsNusyYxioIbi9O4NnId5Nga5AR78VsKPr0sJuLFpZXjC5ZsL5edbIknumnF/bPI6FQFUjGNPt6Nr+vX+mvQLS6xJcCUq9r/ML6/D35raAO0ra3j63xgkA1Hsr36v0ram0YgTondJ0Xm0ekcfvXn7a2B+O2+FObIRNKLLOmXPHtmtkyQ7Qit95DO5lnQYyWUkYyMcfhSMJkuOH8SP0T840m6NrOf5kHDP3jlXyO0kK/8Aci4/Gv3jaQ0+EUX9zQkhg3k4/nTdpvy8P2BkZxWl1Dr4MM0DjiK3mhd5y1Y40X0jWebY40OyOHLhyrXoXX9bHGvRHPPLvrWZ5dzv+s7ns415zzxnn51vNC7zlqxxrUsaK3iFptKKNXFsDnQwo4cBR7C8Tk8KGpQ2OIyKGFHDgOFDCgY5cOXReylgRPIHA8OyB+nRvNC7zlqxxrU0SM3iVrlQGOArPfWGjVh5itKKFHgPmv/EACgQAQABAwIFBQEBAQEAAAAAAAERACExQVEQYXGBkSAwobHw0cFA4f/aAAgBAQABPyGhC9ZA3WmcFD4Xr4FIPypSasZaWLgR2exhlvEq8Ad0q1c8CG0QwzNEkkMSSSNvdKSLiEJvW6/4lWHZgC6tbACpZ+Q068I3LLzT/JI70IgAQBpwtXJGWvL3A7ZoZOOb3HYEwU1CZMAsaVew2wejdmi7It6ZqWahwqUFIJZEFFAhsCq50voLIsgM268AkTogEGvWsk8eAEw1YByYXRNbozSkkxDB1LPigfyakKjcpp2ztMTMSUaPATUlbeATofRPWYnrLX3mnI+ANxQ0Cd65/CTKNGZbYV6b7DHkqdKUdIrLEBZNyo4KJYQsfPz7a15amoy9zxZoF2C78w7PB4bhHWMHdgqTQDapLT+peJYJ8VuL9DdX7u+v1Nq+F+uBT93f0T+VrBgi63KJdNSLEF2l+KeL6uUXJYmLjQb0YCtqwST3pSn0ME8tGZNLw9a/E5cZ+hSNpxNJ8m8IKc2GptGtXukZk+RkjoZo6dgVhtQQKtzB2lD5Wcl7KXnSbIGxCWFqG9GS5kUYUs4fL20/smSihHSB88AGj1h/m7TJAIev3FeGhK3X+rgS++QQ8Aw3RFJRKnRqC0s7lf3d9NaiA8r/AMpEoFHe1fHNyE/Vfu7+ifyIGUwUQ4GNyL/soJd4Bi8YLt2kIFCd0xgbNAvmCUXmsAdNP6nL0Tz0MxPQj5EROtCTprZy0J1VAiWVCgYvjYovXMlpzJmGnOa8jYLs3WKINMAvrZaF4vF7UOZlMl3OoMzEa1K9eQSiNLIztepPpqjinY51fo6lMwyNJ1inu61wKN7NJrUxEjhfMT4qdyr0aLbyrU4OoOQL89KmnrLsfNvDh0nirw7uz4eKjw2EptpXjlwQix6AGVpZ1KG6WvLPfhJRCHmpfRwQwrV5I7kvbnUOQnELDRMVMbqlm4g82Ve1TV4LzJ1DNtZpm8wCWAFNOY0kwLD0oyBLnLRktOricwUwHqPEGXkKxh1IJL2WoUaYBHkFMetNncJSR7p2T/wbNJnJSQML/rFbr4ZIgO8GvWpwHmyDMxERRRBKr6oeEw6Y1TjPahF5PGcl0oHQvFM5wZBRwsM72lNJegobUK63hpzWs3irrcLeazQ49ACTZ+pJqz4QvBkkW7KNJY7qbICCdXa0Jp2Si6TAyEzC7FSLiqmBz5rykVeBDJQnHRigMguwIIIkCM4zin2RTyQK9Jp6m+uyEQp9chrjC7Eu4mlTkncspNmwdn0m/eTgG61IqWPy+516ZrLBd2KkvBHkI7AHnhDxhSHaWrvwYOQ0k4wVibASXjPrgKESzJ657I952P2Lprl9qULKQ1KkWHS+IezNLQYMMEzBC80+3QF5ndGF96dcYTARKQdyn9S5Fgxhv7ZgE5M5651DFIjQwzwFAZbZ+QZ7zwBABVsBrTImQGWgfXc8bgktL2BQqiYgwuGQrfNlJEnmSgAwS6UFYw3jdNBJwrhU5Y9CFW4LAk0WNSrCY8tKRJmb73mvjMgYkFuusBLOdzSYQXmr+dEEUmvXs9KDczv0xoAicl3Kwm8aBABLN76ZKudCakFoMvlTbIECRi2ZWZxUsu62Utw2Zh3ogANg2NgLjY8U34LQSFuouqutwuR0CSym7xntFuZRJbRJzmOVeaufY8uehE0iEJyZXZqjDkjWrBOhZ5K0Gl2xAhIqMjB4p55gm02lUk+gxyllIXXn7jtuFUm3yKCtoiHwpEvcB5/wrQ1RIPp2HoRQJSSYa1E5AFTwEEDFyuUjDt2mgSYwAkdaDEGcw+CgGJjLDLvVmMOi7utJ5MVHcGlyj6UCyCJRyNpp006QfNNNF2Anu70qwRsGDYqPsCXd6yL9iYd6scRsYGxWOliGDlwOguC4I7wbMDCs2mmaZpFoKYBN22axhoEYrSiyJi9ABRUDYo0B4GD2v//aAAwDAQACAAMAAAAQupSd8/0X33x43T9Ry388888xu4pxcpAarw/cCWZjX1w34wffHrtqRCtoBlUCXonEwTiCM7Ohm4qmHTgPF5F7Ok+88888/8QAJxEBAAIBAgQGAwEAAAAAAAAAAQARMSFBUaHR8BBhcZHB4SCBsfH/2gAIAQMBAT8QyUXBE6hbggQFuJh4afAanVxLUVBqb3tBUkuVYhhLGDodSVmjKi1XiPBS4jT1Phigtg6CebBHH5HXcVpGdmn9QENpQFgy2ickzA8z5nNv8nIRIpwP5GaM3b9whqg6a5/c3xXG5zvwwKhSmvt3UqlYd9IDQ99s0ruGEzPrNSr8B6BlIKIoJoZ9YnCwnxQAWwovaWT25reJXzv6xaio22h3UDahMkXKh6QUGo534YTVtd99II3V9wKzH1+/ODiaa17Mq7ygFOsMpeelRrdqv66eDIKnjNTUeVHLSK7E7wPgi1uz/OnOBN8Xn1gAOzTpzggC0PcXBBeEGAMK5fuAbe2nR94VQ2fhjqzzPeOnO9zCr4Tsen5f/8QAJxEBAAIABAUEAwEAAAAAAAAAAQARITFBYVFxocHwEJGx0SCB4fH/2gAIAQIBAT8Qr5gGbENiuMuAVcDRzLPRaDAzlKq1w4VrFCMGS44Ckg1GDlLhalxTLOHBYZwjyO5EVEEtm3ETP8luLbmD3H4Tej4huw1TbOsO06D6nTk6uETePeAxZVR/IkjZMdppluHOdJ3IhaLjZ8Hxl2vN59xG08P8mLVRHMm3BMQ9F7YloLYAli5coaa4HZ0VThzlEUGV6QKGWnKHtm3WLVQlAkZMHK0VFCLjpO5GLovzz7iZer+S6nPy5bbRc5w+T3ljTLz4l1wYRUNeXcL1S6/v36ECLOEw1Zvb1xhut6n0BBqx7/fSKlcFafUUV8Y7b9IpVpGnDRXmEXdEuNw5r4a/rSLCdm+257RAB4dyGKeftKjYaVCjdcesAZfl/8QAJRABAQACAgIBBAMBAQAAAAAAAREAITFBUWEQcYGRoSAw8LHx/9oACAEBAAE/EMavkMFyhAPbj1rMqeXH7QPTlGMU1Z4Wr9ZMI/YTT9ip8r/R5rtHABSqAqG94zyVeSCbNEGzdswKnlTwSkRL7o6/seMIYSREBTytfM6+b/fAybBih0ALnkqQq4D2vK6E1s5MAxCBeaP1+4GDfEFAGgDFguB+OfzKfhId2HomESIjwnyUwBSpVIVk3iLY1MWi2InfnC3JHhgACQBpEZGlxg5niIVZsAN6hJlCA/jCyAQN6LDIk9hFDaADbmoJbSgLREP7+OZlFmAWVXp4yUSRgwIKUs1ipAlkhBoCI02jciZ9xGZ9Eh5hfWChq+3wif7rNSg+7D7ADzN5cWpTbAuIUIMoKIiKNzr5BjdP4ABNeQw984Ug46wMApqLulqDhddqhorWorT0mTBbFFGDZg5VIOZKMs6DKGg1aI2tgKmiBNYOaNvo9/1qzlTG7vSV5BO8P3jkOYnTY4b6r4IFAlEsflge3PKVdEL+VzT8FJwp+v61fynZQv8AI8PjR/tef8NVjd4O4FGP1QxH3SIgF2JFnUEuairmSAe4IjSXbmnGBg0jiBGtr3g1GPEXU0HtxAFGBv2z/m+M9fAMsmhDNEavq4e+8UlkM84EAdkNNCZ0ZBu8ACjokbzupZNDtizW9ZPqb7lsKQIoEjHDDtB7dpJLoDK4dj44fgTipQ6bX+vSGl8ChtXEdC6wRBoj2YTxDKp2+AbTQCuEaGiU/aT7/CjhOXRtH1t+/AmCNUBdDP8An9Pissb9oAQ8BXWGkHvQiR/CfEUD6SdgC/lfjAJg1wEImOHmhyv/AAK/Gf7Xnjyq/FiNxfkaxEZeYkdPp+zCQCXCQABRAAFVy22LhFdbr0XjHFNQgNBtNdYiGER4C+M/3fGevgGsMgOlhcNNiEMOAVMCu+gntQZB51gdck8FqpsURwo0eGydHAY+mMgGutTCu+C67xLmhroIaCNqqBRMaTdHYAooLWZEaq8aXtgdslGxQFEcR/MGioHDNrRTzhhKoz7oUKi4OVAuVqhFiTl0hE5w1GeScNPDQmlGaorhoFK3diprznkT5XToDmPeM8BvFY7mgMCkdFGmJTE6TVZNVFK+QXFX3Yyg6PRD18FhIMKqHgAVcQ6B55pHh2Z5fw2g6Pph+Pz+/gvyUWkX2UJ2mmyPFd6MQLTQOBQiuEUhSW4QyxBxpyOGYcIFDY6XQuDDYCUZOK82QY0UEN6whTmohFOgY5IMhtewAJDsCwpvHH6DVodg1qxCNi2CsDog2+u8uamYCitGbjgWogApENmnY4nCldLRDoHZEQdOjkKMnUXUHegJZoxAL6NhKdNXkqNTCowhj9/YDbJirgYHK1i/wxWyCAt1M6+AfDFVON6ogbCK0NsDa7kxk9FtRehqjw6Y6ZARxwCfbFj7G6sGUdoj3twu1yAonEkNGzDUZzxbbSXbqAD5gpeAJxYr42ksHtj2mZCHBEABROEzo1yOV2v/AKnw29jA6FQLsukHhxtLRhJdABGlRXEsdLWGJxwV1s84I7CFiQNoIobBHFn9oYS/l3iF44cvERXahBYzS7hx/BScxphVGgDtx9VTUJfqbn4JywYCQKMAqvgO3KADdqIJ9ZHn4C2BEhWFBYLPTlLV5otWlKUuaMY40ur57yxs9KqUCjER9iYx7/eQ8v5wAeX84g9/vCec094cCB6w0gnY3IXn95TzgB3+8ePgG8ZOBNuFlHEPr7DIEAE1hDIMbnVMaZqhiQ8mr2MAyTqaKyIIgN9YSNXOI0xQhKDjAAv3fFSEF6WY8xwrtFpBseTJMmTIf5yZMmT6/n5QKpYP34ZzsjZN41rkGT5235xFT6d6pRudL+DrnAKp4A7fWHoRoTmhxOU3ZCBgQ+JHguNRNhJZO5grRr5dqBHFuMEnUkFkJQUNIBsSnDh569oGtCEBxTGj40ASqVaTeCJsSkCwkoJUPRDE+vZmaCKQowgGu8H0ZGEcQ2sdREcjkzaJEFYaG9KcUgfcm3UuLaGiTeXrMKCVDNWqwOCQxIoHk4Rko5iwlrlnCxQmoFD5MRqCU/RBLRtXRBMdRglJ0QsBFOFlCqHn9SbJhGpq4sGc08ghQBBW6zr4B8fuY26mSdQNUQ1HEXEE8E5tFSeBtJ3nHBqs0OwOxK2Mb7StDjrHILUR1hRK62e8NDwOe8AFlsDduEs/A5lGEOqnF/rS4xOG4Psil2sF84ypnU49rD9gxIKOXX+IYcF2lfmg+oT7wJ83wSwWEp4Yu/eI0REwpsYiaxRXYsQZQehh+DFAnMnB9LZ6uAL4UC4GKh4XCljCBTRdNLXjy4E2IFAILFUFPpkgCIDg5dfy5xxHhIpI05aPTww/Jwh5qbPVwsxIut50LvEQJS4aYm3tx4HVE4+jRo0eMNSEoavha26N85xGnQNwKafZjCTVALS6NGjR4zkuU4pyQaNGjHjHn0aFbTlVuuk+Fw2ha36Gz1cfMAq7ONpcF3CwBXFfOeIaySOIdTPxNLS2XxgGdK6WqCcrzleFRyu2Br+r/9k="
- }
- },
- "cell_type": "markdown",
- "metadata": {
- "toc-hr-collapsed": false
- },
- "source": [
- " \n",
- "Author: [Filipe Guimaraes](mailto:f.guimaraes@fz-juelich.de)\n",
- "--------------------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Create your own Jupyter pyenv-Kernel\n",
- "\n",
- "Often the standard kernel do not provide all features you need for your work. This might be that certain modules are not loaded or packages are not installed.\n",
- "With your own kernel you can overcome that problem easily and define your own environment, in which you work.\n",
- "\n",
- "This notebook shows you how you can build your own kernel for a **pyenv environment**.\n",
- "\n",
- "--------------------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Building your own Jupyter pyenv-kernel is a four-step process\n",
- "\n",
- "1. **[Download/Install pyenv](#install)**: To start from scratch, and run the full installation.\n",
- "2. **[Create and setup environment](#environment)**: To create an(other) environment in an existing pyenv setup. \n",
- "If `pyenv` is already installed, start here.\n",
- "3. **[Create/Edit launch script for the Jupyter kernel](#kernel)**: To setup an environment to be run via Jupyter. \n",
- "If the environment already exists, start here.\n",
- "4. **[Create/Edit Jupyter kernel configuration](#json)**: To attach your user to an existing environment via Jupyter. \n",
- "If the kernel launch script was already created (e.g., by some other user in the project), start here."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id='settings'></a>\n",
- "### Settings"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To simplifly the process, it is convenient to define a **PYENV_ROOT** path for the central pyenv installation and put on the PATH. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Important**: It is recommended to use a folder inside the `$PROJECT` file system, as the `$HOME` quota is low. It is also useful to share installation for different users in a single project."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "export PYENV_ROOT=${PROJECT_<projectid>}/${USER}/.pyenv\n",
- "export PATH=\"$PYENV_ROOT/bin:$PATH\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Also the environment name can be set in an environment variable **PYENV_ENV** to simplify the process:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "export PYENV_ENV=my_env"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<a id='install'></a>\n",
- "## 1. Download/Install pyenv"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Installing and setting up pyenv from scratch is very simple. A simple command is needed to install pyenv in **$PYENV_ROOT**:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "curl https://pyenv.run | bash"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<a id='environment'></a>\n",
- "## 2. Create and setup pyenv environment"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The following steps describe how to create and setup a new pyenv environment to be used as a jupyter kernel. They can be repeated if multiple environments are required."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "For these steps, make sure to have a clean environment before starting this process. This can be obtained by running `module purge`:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "$ module purge\n",
- "$ module list\n",
- "\n",
- "Currently Loaded Modules:\n",
- " 1) Stages/2020 (S)\n",
- "\n",
- " Where:\n",
- " S: Module is Sticky, requires --force to unload or purge"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 2.1 - Activate pyenv and virtual envs"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Activate pyenv and the virtual environments by running:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "eval \"$(pyenv init --path)\"\n",
- "eval \"$(pyenv init -)\"\n",
- "eval \"$(pyenv virtualenv-init -)\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 2.2 - Install a python version (e.g. `3.10.1`)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pyenv install 3.10.1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Note: This step may take a few minutes to complete the installation."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 2.3 - Create a new environment **$PYENV_ENV** (defined in **[Settings section](#settings)**)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The environment is created using the python version installed above"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pyenv virtualenv 3.10.1 $PYENV_ENV"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 2.4 - Activate and setup the environment"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Jupyter requires the `ipykernel` module and its dependencies. To install them, first activate the environment:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pyenv activate $PYENV_ENV"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "When the environment is successfully activated, the name of the environment is shown between parenthesis in the command line, e.g. `(my_env)`. (To deactivate the environment, use `pyenv deactivate $PYENV_ENV`.)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The python version can be checked using"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "(my_env)$ python --version\n",
- "Python 3.10.1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The list of python modules is still empty"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "(my_env)$ pip list\n",
- "Package Version\n",
- "---------- -------\n",
- "pip 21.2.4\n",
- "setuptools 58.1.0"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To create a Jupyter kernel, the `ipykernel` and its dependencies are required. `pip` can be used to install it:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pip install ipykernel"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Many modules are installed:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "(my_env)$ pip list\n",
- "Package Version\n",
- "----------------- -------\n",
- "backcall 0.2.0\n",
- "debugpy 1.5.1\n",
- "decorator 5.1.1\n",
- "entrypoints 0.3\n",
- "ipykernel 6.6.1\n",
- "ipython 7.31.0\n",
- "jedi 0.18.1\n",
- "jupyter-client 7.1.0\n",
- "jupyter-core 4.9.1\n",
- "matplotlib-inline 0.1.3\n",
- "nest-asyncio 1.5.4\n",
- "parso 0.8.3\n",
- "pexpect 4.8.0\n",
- "pickleshare 0.7.5\n",
- "pip 21.1.1\n",
- "prompt-toolkit 3.0.24\n",
- "ptyprocess 0.7.0\n",
- "Pygments 2.11.2\n",
- "python-dateutil 2.8.2\n",
- "pyzmq 22.3.0\n",
- "setuptools 56.0.0\n",
- "six 1.16.0\n",
- "tornado 6.1\n",
- "traitlets 5.1.1\n",
- "wcwidth 0.2.5"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<a id='kernel'></a>\n",
- "## 3. Create/Edit launch script for the Jupyter kernel"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The following steps describe how to create and configure the launch script of a new Jupyter kernel using a pyenv environment. If the environment was created in the $PROJECT folder, many users of the project can follow these steps to create the kernel. The steps assume the variables described in the **[Settings section](#settings)** are set up."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id='launch'></a>\n",
- "* 3.1 - Create kernel script to allow access to the pyenv environment"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "echo '#!/bin/bash\n",
- "\n",
- "module purge\n",
- "\n",
- "export PYENV_ROOT='\"$PYENV_ROOT\"'\n",
- "export PATH='\"$PYENV_ROOT\"'/bin:'\"$PATH\"'\n",
- "eval \"$(pyenv init --path)\"\n",
- "eval \"$(pyenv init -)\"\n",
- "eval \"$(pyenv virtualenv-init -)\"\n",
- "\n",
- "# Activate your Python virtual environment\n",
- "pyenv activate '\"${PYENV_ENV}\"'\n",
- "\n",
- "exec python -m ipykernel $@' > ${PYENV_ROOT}/versions/${PYENV_ENV}/kernel.sh"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Add executable permission to the script:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "chmod +x ${PYENV_ROOT}/versions/${PYENV_ENV}/kernel.sh"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "## 4. Create/Edit Jupyter kernel configuration"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "These steps describe how to create a Jupyter kernel configuration file, to be able to access the environment via a Jupyter notebook. To access an existing pyenv environment located in **$PROJECT**, only these steps are necessary. The steps assume the variables described in the **[Settings section](#settings)** are set up."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 4.1 - Create a folder for the kernel"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mkdir -p $HOME/.local/share/jupyter/kernels/pyenv_${PYENV_ENV}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* 4.2 - Create and adjust the kernel.json file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "echo '{\n",
- " \"argv\": [\n",
- " \"'\"${PYENV_ROOT}\"'/versions/'\"${PYENV_ENV}\"'/kernel.sh\",\n",
- " \"-f\",\n",
- " \"{connection_file}\"\n",
- " ],\n",
- " \"display_name\": \"pyenv_'\"${PYENV_ENV}\"'\",\n",
- " \"language\": \"python\"\n",
- "}' > $HOME/.local/share/jupyter/kernels/pyenv_${PYENV_ENV}/kernel.json"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Restart of JupyterLab might be necessary to see the kernel in the kernel selection overview."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Bash",
- "language": "bash",
- "name": "bash"
- },
- "language_info": {
- "codemirror_mode": "shell",
- "file_extension": ".sh",
- "mimetype": "text/x-sh",
- "name": "bash"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/001-Jupyter/List_JupyterExtensions.ipynb b/001-Jupyter/List_JupyterExtensions.ipynb
deleted file mode 100644
index aaf035596cf9ab9ee2f9bffe3fac6805543cb8d7..0000000000000000000000000000000000000000
--- a/001-Jupyter/List_JupyterExtensions.ipynb
+++ /dev/null
@@ -1,458 +0,0 @@
-{
- "cells": [
- {
- "attachments": {
- "67258d94-84e6-4a0c-ae8f-c74332ec082e.jpg": {
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B/ZLMQqjiSe6m2ds982/82YfT8h5dMUci6biX5WTyJ7vZ0B7meO3QnGqVgoreW80c8fLXG2ofsB43WRDyZTkfO7X9ev8ATXo6t1hN9nTjz8M8s+VPC0/yiHSwCscH2VMElVjCdMgH0TjNJruMawGU6W455VHqnxvF1r2W5cvCoXa4XTMMxlctqH5UJYm1IeRxj5vrEGz+vW4Hb0SYZPyxxFfIbOjQ+LyFv7Vpurg7r/CTsp7OmPaW0o9JXtQ27c/xN/bp2OsQiaQ3fATDs/w351s62nuYtlxSiQzT22FXUOQGrl/tWwEN01r1iCWWbQgy+kpjnyzn31uXvC8bT7pY4ypA7PJk9IeOrlUe92l+8PNMj7P0Z0hc4815Dieea2Hi5d4Lt5o2gIGlcamGPZWzLZroG5a+mjmi4atI14BHsqC6N67q+02tDAQNGjJ99Nbi7dTFfAaN6iJuhg40+kTRgkvDIjzMixxlSAMcmX0lP2uVXr2+02uoYUcR28joWlccyABnSOVTM9zvc2ZmcGRWMcnkAOyOfA+FbPjmvpZhf7P3zEhew/Z4rw+1UXVb9us9ZSIlsNusyYxioIbi9O4NnId5Nga5AR78VsKPr0sJuLFpZXjC5ZsL5edbIknumnF/bPI6FQFUjGNPt6Nr+vX+mvQLS6xJcCUq9r/ML6/D35raAO0ra3j63xgkA1Hsr36v0ram0YgTondJ0Xm0ekcfvXn7a2B+O2+FObIRNKLLOmXPHtmtkyQ7Qit95DO5lnQYyWUkYyMcfhSMJkuOH8SP0T840m6NrOf5kHDP3jlXyO0kK/8Aci4/Gv3jaQ0+EUX9zQkhg3k4/nTdpvy8P2BkZxWl1Dr4MM0DjiK3mhd5y1Y40X0jWebY40OyOHLhyrXoXX9bHGvRHPPLvrWZ5dzv+s7ns415zzxnn51vNC7zlqxxrUsaK3iFptKKNXFsDnQwo4cBR7C8Tk8KGpQ2OIyKGFHDgOFDCgY5cOXReylgRPIHA8OyB+nRvNC7zlqxxrU0SM3iVrlQGOArPfWGjVh5itKKFHgPmv/EACgQAQABAwIFBQEBAQEAAAAAAAERACExQVEQYXGBkSAwobHw0cFA4f/aAAgBAQABPyGhC9ZA3WmcFD4Xr4FIPypSasZaWLgR2exhlvEq8Ad0q1c8CG0QwzNEkkMSSSNvdKSLiEJvW6/4lWHZgC6tbACpZ+Q068I3LLzT/JI70IgAQBpwtXJGWvL3A7ZoZOOb3HYEwU1CZMAsaVew2wejdmi7It6ZqWahwqUFIJZEFFAhsCq50voLIsgM268AkTogEGvWsk8eAEw1YByYXRNbozSkkxDB1LPigfyakKjcpp2ztMTMSUaPATUlbeATofRPWYnrLX3mnI+ANxQ0Cd65/CTKNGZbYV6b7DHkqdKUdIrLEBZNyo4KJYQsfPz7a15amoy9zxZoF2C78w7PB4bhHWMHdgqTQDapLT+peJYJ8VuL9DdX7u+v1Nq+F+uBT93f0T+VrBgi63KJdNSLEF2l+KeL6uUXJYmLjQb0YCtqwST3pSn0ME8tGZNLw9a/E5cZ+hSNpxNJ8m8IKc2GptGtXukZk+RkjoZo6dgVhtQQKtzB2lD5Wcl7KXnSbIGxCWFqG9GS5kUYUs4fL20/smSihHSB88AGj1h/m7TJAIev3FeGhK3X+rgS++QQ8Aw3RFJRKnRqC0s7lf3d9NaiA8r/AMpEoFHe1fHNyE/Vfu7+ifyIGUwUQ4GNyL/soJd4Bi8YLt2kIFCd0xgbNAvmCUXmsAdNP6nL0Tz0MxPQj5EROtCTprZy0J1VAiWVCgYvjYovXMlpzJmGnOa8jYLs3WKINMAvrZaF4vF7UOZlMl3OoMzEa1K9eQSiNLIztepPpqjinY51fo6lMwyNJ1inu61wKN7NJrUxEjhfMT4qdyr0aLbyrU4OoOQL89KmnrLsfNvDh0nirw7uz4eKjw2EptpXjlwQix6AGVpZ1KG6WvLPfhJRCHmpfRwQwrV5I7kvbnUOQnELDRMVMbqlm4g82Ve1TV4LzJ1DNtZpm8wCWAFNOY0kwLD0oyBLnLRktOricwUwHqPEGXkKxh1IJL2WoUaYBHkFMetNncJSR7p2T/wbNJnJSQML/rFbr4ZIgO8GvWpwHmyDMxERRRBKr6oeEw6Y1TjPahF5PGcl0oHQvFM5wZBRwsM72lNJegobUK63hpzWs3irrcLeazQ49ACTZ+pJqz4QvBkkW7KNJY7qbICCdXa0Jp2Si6TAyEzC7FSLiqmBz5rykVeBDJQnHRigMguwIIIkCM4zin2RTyQK9Jp6m+uyEQp9chrjC7Eu4mlTkncspNmwdn0m/eTgG61IqWPy+516ZrLBd2KkvBHkI7AHnhDxhSHaWrvwYOQ0k4wVibASXjPrgKESzJ657I952P2Lprl9qULKQ1KkWHS+IezNLQYMMEzBC80+3QF5ndGF96dcYTARKQdyn9S5Fgxhv7ZgE5M5651DFIjQwzwFAZbZ+QZ7zwBABVsBrTImQGWgfXc8bgktL2BQqiYgwuGQrfNlJEnmSgAwS6UFYw3jdNBJwrhU5Y9CFW4LAk0WNSrCY8tKRJmb73mvjMgYkFuusBLOdzSYQXmr+dEEUmvXs9KDczv0xoAicl3Kwm8aBABLN76ZKudCakFoMvlTbIECRi2ZWZxUsu62Utw2Zh3ogANg2NgLjY8U34LQSFuouqutwuR0CSym7xntFuZRJbRJzmOVeaufY8uehE0iEJyZXZqjDkjWrBOhZ5K0Gl2xAhIqMjB4p55gm02lUk+gxyllIXXn7jtuFUm3yKCtoiHwpEvcB5/wrQ1RIPp2HoRQJSSYa1E5AFTwEEDFyuUjDt2mgSYwAkdaDEGcw+CgGJjLDLvVmMOi7utJ5MVHcGlyj6UCyCJRyNpp006QfNNNF2Anu70qwRsGDYqPsCXd6yL9iYd6scRsYGxWOliGDlwOguC4I7wbMDCs2mmaZpFoKYBN22axhoEYrSiyJi9ABRUDYo0B4GD2v//aAAwDAQACAAMAAAAQupSd8/0X33x43T9Ry388888xu4pxcpAarw/cCWZjX1w34wffHrtqRCtoBlUCXonEwTiCM7Ohm4qmHTgPF5F7Ok+88888/8QAJxEBAAIBAgQGAwEAAAAAAAAAAQARMSFBUaHR8BBhcZHB4SCBsfH/2gAIAQMBAT8QyUXBE6hbggQFuJh4afAanVxLUVBqb3tBUkuVYhhLGDodSVmjKi1XiPBS4jT1Phigtg6CebBHH5HXcVpGdmn9QENpQFgy2ickzA8z5nNv8nIRIpwP5GaM3b9whqg6a5/c3xXG5zvwwKhSmvt3UqlYd9IDQ99s0ruGEzPrNSr8B6BlIKIoJoZ9YnCwnxQAWwovaWT25reJXzv6xaio22h3UDahMkXKh6QUGo534YTVtd99II3V9wKzH1+/ODiaa17Mq7ygFOsMpeelRrdqv66eDIKnjNTUeVHLSK7E7wPgi1uz/OnOBN8Xn1gAOzTpzggC0PcXBBeEGAMK5fuAbe2nR94VQ2fhjqzzPeOnO9zCr4Tsen5f/8QAJxEBAAIABAUEAwEAAAAAAAAAAQARITFBYVFxocHwEJGx0SCB4fH/2gAIAQIBAT8Qr5gGbENiuMuAVcDRzLPRaDAzlKq1w4VrFCMGS44Ckg1GDlLhalxTLOHBYZwjyO5EVEEtm3ETP8luLbmD3H4Tej4huw1TbOsO06D6nTk6uETePeAxZVR/IkjZMdppluHOdJ3IhaLjZ8Hxl2vN59xG08P8mLVRHMm3BMQ9F7YloLYAli5coaa4HZ0VThzlEUGV6QKGWnKHtm3WLVQlAkZMHK0VFCLjpO5GLovzz7iZer+S6nPy5bbRc5w+T3ljTLz4l1wYRUNeXcL1S6/v36ECLOEw1Zvb1xhut6n0BBqx7/fSKlcFafUUV8Y7b9IpVpGnDRXmEXdEuNw5r4a/rSLCdm+257RAB4dyGKeftKjYaVCjdcesAZfl/8QAJRABAQACAgIBBAMBAQAAAAAAAREAITFBUWEQcYGRoSAw8LHx/9oACAEBAAE/EMavkMFyhAPbj1rMqeXH7QPTlGMU1Z4Wr9ZMI/YTT9ip8r/R5rtHABSqAqG94zyVeSCbNEGzdswKnlTwSkRL7o6/seMIYSREBTytfM6+b/fAybBih0ALnkqQq4D2vK6E1s5MAxCBeaP1+4GDfEFAGgDFguB+OfzKfhId2HomESIjwnyUwBSpVIVk3iLY1MWi2InfnC3JHhgACQBpEZGlxg5niIVZsAN6hJlCA/jCyAQN6LDIk9hFDaADbmoJbSgLREP7+OZlFmAWVXp4yUSRgwIKUs1ipAlkhBoCI02jciZ9xGZ9Eh5hfWChq+3wif7rNSg+7D7ADzN5cWpTbAuIUIMoKIiKNzr5BjdP4ABNeQw984Ug46wMApqLulqDhddqhorWorT0mTBbFFGDZg5VIOZKMs6DKGg1aI2tgKmiBNYOaNvo9/1qzlTG7vSV5BO8P3jkOYnTY4b6r4IFAlEsflge3PKVdEL+VzT8FJwp+v61fynZQv8AI8PjR/tef8NVjd4O4FGP1QxH3SIgF2JFnUEuairmSAe4IjSXbmnGBg0jiBGtr3g1GPEXU0HtxAFGBv2z/m+M9fAMsmhDNEavq4e+8UlkM84EAdkNNCZ0ZBu8ACjokbzupZNDtizW9ZPqb7lsKQIoEjHDDtB7dpJLoDK4dj44fgTipQ6bX+vSGl8ChtXEdC6wRBoj2YTxDKp2+AbTQCuEaGiU/aT7/CjhOXRtH1t+/AmCNUBdDP8An9Pissb9oAQ8BXWGkHvQiR/CfEUD6SdgC/lfjAJg1wEImOHmhyv/AAK/Gf7Xnjyq/FiNxfkaxEZeYkdPp+zCQCXCQABRAAFVy22LhFdbr0XjHFNQgNBtNdYiGER4C+M/3fGevgGsMgOlhcNNiEMOAVMCu+gntQZB51gdck8FqpsURwo0eGydHAY+mMgGutTCu+C67xLmhroIaCNqqBRMaTdHYAooLWZEaq8aXtgdslGxQFEcR/MGioHDNrRTzhhKoz7oUKi4OVAuVqhFiTl0hE5w1GeScNPDQmlGaorhoFK3diprznkT5XToDmPeM8BvFY7mgMCkdFGmJTE6TVZNVFK+QXFX3Yyg6PRD18FhIMKqHgAVcQ6B55pHh2Z5fw2g6Pph+Pz+/gvyUWkX2UJ2mmyPFd6MQLTQOBQiuEUhSW4QyxBxpyOGYcIFDY6XQuDDYCUZOK82QY0UEN6whTmohFOgY5IMhtewAJDsCwpvHH6DVodg1qxCNi2CsDog2+u8uamYCitGbjgWogApENmnY4nCldLRDoHZEQdOjkKMnUXUHegJZoxAL6NhKdNXkqNTCowhj9/YDbJirgYHK1i/wxWyCAt1M6+AfDFVON6ogbCK0NsDa7kxk9FtRehqjw6Y6ZARxwCfbFj7G6sGUdoj3twu1yAonEkNGzDUZzxbbSXbqAD5gpeAJxYr42ksHtj2mZCHBEABROEzo1yOV2v/AKnw29jA6FQLsukHhxtLRhJdABGlRXEsdLWGJxwV1s84I7CFiQNoIobBHFn9oYS/l3iF44cvERXahBYzS7hx/BScxphVGgDtx9VTUJfqbn4JywYCQKMAqvgO3KADdqIJ9ZHn4C2BEhWFBYLPTlLV5otWlKUuaMY40ur57yxs9KqUCjER9iYx7/eQ8v5wAeX84g9/vCec094cCB6w0gnY3IXn95TzgB3+8ePgG8ZOBNuFlHEPr7DIEAE1hDIMbnVMaZqhiQ8mr2MAyTqaKyIIgN9YSNXOI0xQhKDjAAv3fFSEF6WY8xwrtFpBseTJMmTIf5yZMmT6/n5QKpYP34ZzsjZN41rkGT5235xFT6d6pRudL+DrnAKp4A7fWHoRoTmhxOU3ZCBgQ+JHguNRNhJZO5grRr5dqBHFuMEnUkFkJQUNIBsSnDh569oGtCEBxTGj40ASqVaTeCJsSkCwkoJUPRDE+vZmaCKQowgGu8H0ZGEcQ2sdREcjkzaJEFYaG9KcUgfcm3UuLaGiTeXrMKCVDNWqwOCQxIoHk4Rko5iwlrlnCxQmoFD5MRqCU/RBLRtXRBMdRglJ0QsBFOFlCqHn9SbJhGpq4sGc08ghQBBW6zr4B8fuY26mSdQNUQ1HEXEE8E5tFSeBtJ3nHBqs0OwOxK2Mb7StDjrHILUR1hRK62e8NDwOe8AFlsDduEs/A5lGEOqnF/rS4xOG4Psil2sF84ypnU49rD9gxIKOXX+IYcF2lfmg+oT7wJ83wSwWEp4Yu/eI0REwpsYiaxRXYsQZQehh+DFAnMnB9LZ6uAL4UC4GKh4XCljCBTRdNLXjy4E2IFAILFUFPpkgCIDg5dfy5xxHhIpI05aPTww/Jwh5qbPVwsxIut50LvEQJS4aYm3tx4HVE4+jRo0eMNSEoavha26N85xGnQNwKafZjCTVALS6NGjR4zkuU4pyQaNGjHjHn0aFbTlVuuk+Fw2ha36Gz1cfMAq7ONpcF3CwBXFfOeIaySOIdTPxNLS2XxgGdK6WqCcrzleFRyu2Br+r/9k="
- }
- },
- "cell_type": "markdown",
- "metadata": {
- "toc-hr-collapsed": false
- },
- "source": [
- "\n",
- "Author: [Jens Henrik Göbbert](mailto:j.goebbert@fz-juelich.de)\n",
- "------------------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "toc-hr-collapsed": false
- },
- "source": [
- "# List of Extensions on Jupyter-JSC\n",
- "--------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If you want to use any of these extensions, feel free to use our [examples](https://github.com/FZJ-JSC/jupyter-jsc-notebooks) as a starting point. \n",
- "\n",
- "You can list the currently installed extensions by running the command in the JupyterLab terminal: `jupyter labextension list`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "JupyterLab v2.1.4\n",
- "Known labextensions:\n",
- " app dir: /gpfs/software/juwels/stages/Devel-2019a/software/Jupyter/2019a.2-gcccoremkl-8.3.0-2019.3.199-Python-3.6.8/share/jupyter/lab\n",
- " @bokeh/jupyter_bokeh v2.0.2 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " @jupyter-voila/jupyterlab-preview v1.1.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " @jupyter-widgets/jupyterlab-manager v2.0.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " @jupyter-widgets/jupyterlab-sidecar v0.5.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " @jupyterlab/git v0.20.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " @jupyterlab/server-proxy v2.1.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " @jupyterlab/toc v4.0.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " @krassowski/jupyterlab-lsp v1.0.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " @krassowski/jupyterlab_go_to_definition v1.0.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " @parente/jupyterlab-quickopen v0.5.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " @pyviz/jupyterlab_pyviz v1.0.4 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " @ryantam626/jupyterlab_code_formatter v1.3.1 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " bqplot v0.5.12 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " dask-labextension v2.0.2 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " ipyvolume v0.6.0-alpha.5 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " itkwidgets v0.27.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " jupyter-leaflet v0.13.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " jupyter-matplotlib v0.7.2 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " jupyter-threejs v2.2.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " jupyter-vue v1.3.2 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " jupyter-vuetify v1.4.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " jupyterlab-control v1.1.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " jupyterlab-dash v0.2.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " jupyterlab-datawidgets v6.3.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " jupyterlab-gitlab v2.0.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " jupyterlab-lmod v0.7.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " jupyterlab-plotly v4.8.1 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " jupyterlab-system-monitor v0.6.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " jupyterlab-theme-toggle v0.5.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " jupyterlab-topbar-extension v0.5.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " jupyterlab_iframe v0.2.2 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " nbdime-jupyterlab v2.0.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " plotlywidget v4.8.1 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
- " pvlink v0.3.1 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n"
- ]
- }
- ],
- "source": [
- "!jupyter labextension list"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Dask (only on HPC Systems) <a class=\"anchor\" id=\"dask\"></a>\n",
- "https://github.com/dask/dask-labextension"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- "<img src=https://raw.githubusercontent.com/dask/dask-labextension/master/dask.png width=\"640\" style=\"float:right\"/>\n",
- "</div>\n",
- "\n",
- "An [extension](https://github.com/dask/dask-labextension) to manage Dask clusters, as well as embed Dask's dashboard plots directly into JupyterLab panes. \n",
- "Watch [this](https://www.youtube.com/watch?feature=player_embedded&v=EX_voquHdk0) video until the end to unterstand how to use Dask in JupyterLab. At the moment we only offer to use the panels inside of JupyterLab. \n",
- "We have introduction notebooks for this extensions [here](https://gitlab.version.fz-juelich.de/jupyter4jsc/j4j_notebooks/tree/master/001-Extensions) (or open the gitlab extension on the left sidebar)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Git\n",
- "https://github.com/jupyterlab/jupyterlab-git"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- "<img src=https://raw.githubusercontent.com/jupyterlab/jupyterlab-git/master/docs/figs/demo-0-10-0.gif width=\"640\" style=\"float:right\"/>\n",
- "</div>\n",
- "\n",
- "A JupyterLab [Git](https://github.com/jupyterlab/jupyterlab-git) extension for version control using git."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Table of Contents\n",
- "https://github.com/jupyterlab/jupyterlab-toc"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- "<img src=https://raw.githubusercontent.com/jupyterlab/jupyterlab-toc/master/toc.gif width=\"640\" style=\"float:right\"/>\n",
- "</div>\n",
- "\n",
- "A [Table of Contents extension](https://github.com/jupyterlab/jupyterlab-toc) for JupyterLab. This auto-generates a table of contents in the left area when you have a notebook or markdown document open. \n",
- "The entries are clickable, and scroll the document to the heading in question."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Jupyter ThreeJS\n",
- "https://github.com/jupyter-widgets/pythreejs"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- "<img src=https://raw.githubusercontent.com/jupyter-widgets/pythreejs/master/screencast.gif width=\"640\" style=\"float:right\"/>\n",
- "</div>\n",
- "\n",
- "A Python / [ThreeJS](https://github.com/jupyter-widgets/pythreejs) bridge utilizing the Jupyter widget infrastructure."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "toc-hr-collapsed": false
- },
- "source": [
- "## Leaflet\n",
- "https://github.com/jupyter-widgets/ipyleaflet"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- "<img src=https://raw.githubusercontent.com/jupyter-widgets/ipyleaflet/master/basemap.gif width=\"640\" style=\"float:right\"/>\n",
- "</div>\n",
- "\n",
- "The [Jupyterlab Leaflet extension](https://github.com/jupyter-widgets/ipyleaflet) enables interactive maps. \n",
- "You can find several example notebooks [here](https://github.com/jupyter-widgets/ipyleaflet/tree/master/examples)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Sidecar\n",
- "https://github.com/jupyter-widgets/jupyterlab-sidecar"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- "<img src=https://raw.githubusercontent.com/jupyter-widgets/jupyterlab-sidecar/master/sidecar.gif width=\"640\" style=\"float:right\"/>\n",
- "</div>\n",
- "\n",
- "A [sidecar](https://github.com/jupyter-widgets/jupyterlab-sidecar) output widget for JupyterLab"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Voilà Preview\n",
- "https://github.com/voila-dashboards/voila"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- "<img src=https://jupyter-jsc.fz-juelich.de/hub/static/images/voila_preview.png width=\"640\" style=\"float:right\"/>\n",
- "</div>\n",
- "\n",
- "[Voilà](https://github.com/voila-dashboards/voila) turns Jupyter notebooks into standalone web applications.\n",
- "\n",
- "Unlike the usual HTML-converted notebooks, each user connecting to the Voilà tornado application gets a dedicated Jupyter kernel which can execute the callbacks to changes in Jupyter interactive widgets. \n",
- "\n",
- "This extension allows you to render a Notebook with Voilà, so you can see how your Notebook will look with it.\n",
- "\n",
- "You can download a test notebook with the following command: \n",
- "```\n",
- " $ wget --no-check-certificate https://jupyter-jsc.fz-juelich.de/static/files/voila_basics.ipynb\n",
- "``` \n",
- "and get a preview of it with the button at the top of your notebook."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Quick Open\n",
- "https://github.com/parente/jupyterlab-quickopen"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- "<img src=https://raw.githubusercontent.com/parente/jupyterlab-quickopen/master/doc/quickopen.gif width=\"640\" style=\"float:right\"/>\n",
- "</div>\n",
- "\n",
- "[Quick Open](https://github.com/parente/jupyterlab-quickopen) allows you to quickly open a file in JupyterLab by typing part of its name. Just click on the lens symbol at the left sidebar. \n",
- "<span style=\"color:darkorange\">Takes a long time on HPC systems.</span>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## JupyterLab LaTeX Extension\n",
- "https://github.com/jupyterlab/jupyterlab-latex"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- "<img src=https://raw.githubusercontent.com/jupyterlab/jupyterlab-latex/master/images/show_preview.png width=\"640\" style=\"float:right\"/>\n",
- "</div>\n",
- "\n",
- "The [LaTeX Extension](https://github.com/jupyterlab/jupyterlab-latex) is an extension for JupyterLab which allows for live-editing of LaTeX documents. \n",
- "[Here](https://annefou.github.io/jupyter_publish/03-latex/index.html) you can find a short example."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Code Formatter\n",
- "https://github.com/ryantam626/jupyterlab_code_formatter"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- "<img src=https://jupyterlab-code-formatter.readthedocs.io/en/latest/_images/demo.gif width=\"640\" style=\"float:right\"/>\n",
- "</div>\n",
- "\n",
- "This is a small Jupyterlab plugin to support using various code formatter on the server side and format code cells/files in Jupyterlab. \n",
- "Please read the [documentation](https://jupyterlab-code-formatter.readthedocs.io/en/latest/index.html)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## IPyVolume\n",
- "https://github.com/maartenbreddels/ipyvolume"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- "<img src=https://cloud.githubusercontent.com/assets/1765949/23901444/8d4f26f8-08bd-11e7-81e6-cedad0a8471c.gif width=\"640\" style=\"float:right\"/>\n",
- "</div>\n",
- "\n",
- "3d plotting for Python in the Jupyter notebook based on IPython widgets using WebGL.\n",
- "Please read the [documentation](https://ipyvolume.readthedocs.io/en/latest/). "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Jupyter Lmod\n",
- "https://github.com/cmd-ntrf/jupyter-lmod"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- "<img src=https://camo.githubusercontent.com/2a1fa198b6b7f35c7b9751664dfe5102fa5aa595/68747470733a2f2f692e696d6775722e636f6d2f3148444837694e2e676966 width=\"640\" style=\"float:right\"/>\n",
- "</div>\n",
- "\n",
- "Jupyter interactive notebook server extension that allows user to interact with environment modules before launching kernels. \n",
- "The extension use Lmod's Python interface to accomplish module related task like loading, unloading, saving collection, etc."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Matplotlib\n",
- "https://github.com/matplotlib/ipympl"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- "<img src=https://raw.githubusercontent.com/matplotlib/ipympl/master/matplotlib.gif width=\"640\" style=\"float:right\"/>\n",
- "</div>\n",
- "\n",
- "Leveraging the Jupyter interactive widgets framework, ipympl enables the interactive features of matplotlib in the Jupyter notebook and in JupyterLab. \n",
- "Besides, the figure canvas element is a proper Jupyter interactive widget which can be positioned in interactive widget layouts. \n",
- "Please read the [documentation](https://matplotlib.org/contents.html)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## NDime\n",
- "https://github.com/jupyter/nbdime"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- "<img src=https://raw.githubusercontent.com/jupyter/nbdime/master/docs/source/images/nbmerge-web.png width=\"640\" style=\"float:right\"/>\n",
- "</div>\n",
- "\n",
- "Tools for diffing and merging of Jupyter notebooks. \n",
- "Please read the [documentation](http://nbdime.readthedocs.io)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Plotly\n",
- "https://github.com/plotly/plotly.py"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div>\n",
- "<img src=https://raw.githubusercontent.com/cldougl/plot_images/add_r_img/plotly_2017.png width=\"640\" style=\"float:right\"/>\n",
- "</div>\n",
- "\n",
- "Plotly's Python graphing library makes interactive, publication-quality graphs. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts. \n",
- "Please read the [documentation](https://plotly.com/python)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Other Extensions useful for Jupyter Users\n",
- "- jupyter_bokeh\n",
- "- jupyterlab-lsp\n",
- "- jupyterlab_go_to_definition\n",
- "- jupyterlab_pyviz\n",
- "- bqplot\n",
- "- itkwidgets\n",
- "- jupyterlab-dash\n",
- "- jupyterlab-gitlab\n",
- "- jupyterlab-control\n",
- "- jupyterlab_iframe\n",
- "- jupyterlab-theme-toggle\n",
- "\n",
- "## Internal Extensions\n",
- "- jupyterlab-datawidgets\n",
- "- jupyterlab-server-proxy\n",
- "- jupyterlab-system-monitor\n",
- "- jupyterlab-topbar-extension\n",
- "- pvlink"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.6.8"
- },
- "toc-autonumbering": false,
- "toc-showcode": true,
- "toc-showmarkdowntxt": false
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/001-Jupyter/List_PythonPackages.ipynb b/001-Jupyter/List_PythonPackages.ipynb
deleted file mode 100644
index a779a24a808b00e4eb13f90089bd3a9e86888610..0000000000000000000000000000000000000000
--- a/001-Jupyter/List_PythonPackages.ipynb
+++ /dev/null
@@ -1,435 +0,0 @@
-{
- "cells": [
- {
- "attachments": {
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"
- }
- },
- "cell_type": "markdown",
- "metadata": {
- "toc-hr-collapsed": false
- },
- "source": [
- "\n",
- "Author: [Jens Henrik Göbbert](mailto:j.goebbert@fz-juelich.de)\n",
- "------------------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "toc-hr-collapsed": false
- },
- "source": [
- "# List of included Python packages\n",
- "-------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This lists the python packages available and installed by the install script for Python, SciPy-Stack and Jupyter:\n",
- " - `$EBROOTPYTHON/easybuild/*.eb`\n",
- " - `$EBROOTSCIPYMINSTACK/easybuild/*.eb`\n",
- " - `$EBROOTJUPYTER/easybuild/*.eb`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "!ls $EBROOTJUPYTER/lib/python3.6/site-packages/ | grep .dist-info > ${PKG_DISTINFO}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "pkg_list = [\n",
- " # Python module\n",
- " (\"setuptools\", \"41.6.0\", \"\"),\n",
- " (\"webencodings\", \"0.5.1\", \"\"),\n",
- " (\"six\", \"1.12.0\", \"\"),\n",
- " (\"decorator\", \"4.4.0\", \"\"),\n",
- " (\"MarkupSafe\", \"1.1.1\", \"markupsafe\"),\n",
- " (\"more-itertools\", \"7.2.0\", \"more_itertools\"),\n",
- " (\"pickleshare\", \"0.7.5\", \"\"),\n",
- " (\"jedi\", \"0.15.1\", \"\"),\n",
- " (\"wcwidth\", \"0.1.7\", \"\"),\n",
- " (\"attr\", \"19.3.0\", \"\"),\n",
- " (\"parso\", \"0.5.1\", \"\"),\n",
- " (\"jinja2\", \"2.10.1\", \"\"),\n",
- " (\"pytz\", \"2019.3\", \"\"),\n",
- " (\"pyparsing\", \"2.2.0\", \"\"),\n",
- " (\"packaging\", \"19.2\", \"\"),\n",
- " (\"urllib3\", \"1.25.6\", \"\"),\n",
- " (\"certifi\", \"2019.9.11\", \"\"),\n",
- " (\"requests\", \"2.22.0\", \"\"),\n",
- " (\"python-dateutil\", \"2.8.1\", \"dateutil\"),\n",
- " (\"Pillow\", \"6.2.1\", \"PIL\"),\n",
- " (\"ply\", \"3.11\", \"\"),\n",
- " (\"pyrsistent\", \"0.15.4\", \"\"),\n",
- " (\"lxml\", \"4.4.1\", \"\"),\n",
- " (\"idna\", \"2.8\", \"\"),\n",
- " (\"chardet\", \"3.0.4\", \"\"),\n",
- " (\"pycparser\", \"2.19\", \"\"),\n",
- " (\"cffi\", \"1.13.2\", \"\"),\n",
- " (\"psutil\", \"5.6.3\", \"\"),\n",
- " (\"SQLAlchemy\", \"1.3.10\", \"sqlalchemy\"),\n",
- " (\"certipy\", \"0.1.3\", \"\"),\n",
- " (\"python-editor\", \"1.0.4\", \"editor\"),\n",
- " (\"Mako\", \"1.1.0\", \"mako\"),\n",
- " (\"alembic\", \"1.2.1\", \"\"),\n",
- " (\"click\", \"7.0\", \"\"),\n",
- " (\"appdirs\", \"1.4.3\", \"\"),\n",
- " (\"cloudpickle\", \"1.2.2\", \"\"),\n",
- " (\"toolz\", \"0.10.0\", \"\"),\n",
- " (\"cryptography\", \"2.8\", \"\"),\n",
- " \n",
- " (\"prompt-toolkit\", \"2.0.10\", \"prompt_toolkit\"),\n",
- " (\"oauthlib\", \"3.1.0\", \"\"),\n",
- " (\"async-generator\", \"1.10\", \"async_generator\"),\n",
- " (\"smmap\", \"0.9.0\", \"\"),\n",
- " (\"typed-ast\", \"1.4.0\", \"typed_ast\"),\n",
- "\n",
- " # SciPy-Stack module\n",
- " (\"cycler\", \"0.10.0\", \"\"),\n",
- " (\"numpy\", \"1.15.2\", \"\"),\n",
- " (\"scipy\", \"1.2.1\", \"\"),\n",
- " (\"sympy\", \"1.4\", \"\"),\n",
- " (\"pandas\", \"0.25.3\", \"\"),\n",
- " (\"mpmath\", \"1.1.0\", \"\"),\n",
- " (\"kiwisolver\", \"1.1.0\", \"\"),\n",
- " (\"backports.functools_lru_cache\", \"1.5\", \"\"),\n",
- " (\"matplotlib\", \"3.1.1\", \"\"),\n",
- " (\"xarray\", \"0.11.3\", \"\"),\n",
- " \n",
- " # Jupyter module\n",
- " (\"pyOpenSSL\", \"19.0.0\", \"OpenSSL\"),\n",
- " (\"entrypoints\", \"0.3\", \"\"),\n",
- " (\"async_generator\", \"1.10\", \"\"),\n",
- " (\"absl-py\", \"0.8.1\", \"absl\"),\n",
- " (\"cryptography\", \"2.8\", \"\"),\n",
- " (\"tornado\", \"6.0.3\", \"\"),\n",
- " (\"bokeh\", \"1.3.4\", \"\"),\n",
- " (\"seaborn\", \"0.9.0\", \"\"),\n",
- " (\"nbformat\", \"4.4.0\", \"\"),\n",
- " (\"param\", \"1.9.2\", \"\"),\n",
- " (\"pyviz_comms\", \"0.7.2\", \"\"),\n",
- " (\"holoviews\", \"1.12.6\", \"\"),\n",
- " (\"alabaster\", \"0.7.12\", \"\"),\n",
- " (\"Babel\", \"2.7.0\", \"babel\"),\n",
- " (\"snowballstemmer\", \"2.0.0\", \"\"),\n",
- " (\"docutils\", \"0.15.2\", \"\"),\n",
- " (\"imagesize\", \"1.1.0\", \"\"),\n",
- " (\"sphinxcontrib-websupport\", \"1.1.2\", \"sphinxcontrib.websupport\"),\n",
- " (\"Sphinx\", \"1.8.5\", \"sphinx\"),\n",
- " (\"pexpect\", \"4.7.0\", \"\"),\n",
- " (\"ipython\", \"7.9.0\", \"IPython\"),\n",
- " (\"ipynb\", \"0.5.1\", \"\"),\n",
- " (\"jupyter_core\", \"4.6.1\", \"\"),\n",
- " (\"retrying\", \"1.3.3\", \"\"),\n",
- " (\"plotly\", \"4.2.1\", \"\"),\n",
- " (\"tikzplotlib\", \"0.8.4\", \"\"),\n",
- " (\"jupyter_client\", \"5.3.4\", \"\"),\n",
- " (\"traitlets\", \"4.3.3\", \"\"),\n",
- " (\"pyzmq\", \"18.1.0\", \"zmq\"),\n",
- " (\"singledispatch\", \"3.4.0.3\", \"\"),\n",
- " (\"ipyparallel\", \"6.2.4\", \"\"),\n",
- " (\"ipykernel\", \"5.1.3\", \"\"),\n",
- " (\"terminado\", \"0.8.2\", \"\"),\n",
- " (\"bleach\", \"3.1.0\", \"\"),\n",
- " (\"mistune\", \"0.8.4\", \"\"),\n",
- " (\"pandocfilters\", \"1.4.2\", \"\"),\n",
- " (\"Pygments\", \"2.4.2\", \"pygments\"),\n",
- " (\"testpath\", \"0.4.4\", \"\"),\n",
- " (\"nbconvert\", \"5.6.1\", \"\"),\n",
- " (\"ipython_genutils\",\"0.2.0\", \"\"),\n",
- " (\"Send2Trash\", \"1.5.0\", \"send2trash\"),\n",
- " (\"notebook\", \"6.0.2\", \"\"),\n",
- " (\"version_information\", \"1.0.3\", \"\"),\n",
- " (\"lesscpy\", \"0.13.0\", \"\"),\n",
- " (\"prometheus-client\", \"0.7.1\", \"prometheus_client\"),\n",
- " (\"jupyterthemes\", \"0.20.0\", \"\"),\n",
- " (\"zipp\", \"0.6.0\", \"\"),\n",
- " (\"importlib_metadata\", \"0.23\", \"\"),\n",
- " (\"jsonschema\", \"3.1.1\", \"\"),\n",
- " (\"jupyterlab_launcher\", \"0.13.1\",\"\"),\n",
- " (\"sphinx_rtd_theme\",\"0.4.3\", \"\"),\n",
- " (\"future\", \"0.18.1\", \"\"),\n",
- " (\"commonmark\", \"0.9.1\", \"\"),\n",
- " (\"recommonmark\", \"0.6.0\", \"\"),\n",
- " (\"jupyterlab\", \"1.2.1\", \"\"),\n",
- " (\"json5\", \"0.8.5\", \"\"),\n",
- " (\"jupyterlab_server\", \"1.0.6\", \"\"),\n",
- " (\"ptyprocess\", \"0.6.0\", \"\"),\n",
- " (\"defusedxml\", \"0.6.0\", \"\"),\n",
- " (\"widgetsnbextension\", \"3.5.1\", \"\"),\n",
- " (\"ipywidgets\", \"7.5.1\", \"\"),\n",
- " (\"ipydatawidgets\", \"4.0.1\", \"\"),\n",
- " (\"traittypes\", \"0.2.1\", \"\"),\n",
- " (\"bqplot\", \"0.11.9\", \"\"),\n",
- " (\"jupyter_bokeh\", \"1.1.1\", \"\"),\n",
- " (\"pythreejs\", \"2.1.1\", \"\"),\n",
- " (\"PyWavelets\", \"1.1.1\", \"pywt\"),\n",
- " (\"imageio\", \"2.6.1\", \"\"),\n",
- " (\"networkx\", \"2.3\", \"\"),\n",
- " (\"scikit-image\", \"0.16.2\", \"skimage\"),\n",
- " (\"ipywebrtc\", \"0.5.0\", \"\"),\n",
- " (\"ipyvolume\", \"0.5.2\", \"\"),\n",
- " (\"branca\", \"0.3.1\", \"\"),\n",
- " (\"ipyleaflet\", \"0.11.4\", \"\"),\n",
- " (\"ipympl\", \"0.3.3\", \"\"),\n",
- " (\"PyYAML\", \"5.1.2\", \"yaml\"),\n",
- " (\"jupyter_nbextensions_configurator\", \"0.4.1\", \"\"),\n",
- " (\"jupyter_latex_envs\", \"1.4.6\", \"latex_envs\"),\n",
- " (\"jupyter_highlight_selected_word\", \"0.2.0\", \"\"),\n",
- " (\"jupyter_contrib_core\", \"0.3.3\",\"\"),\n",
- " (\"jupyter_contrib_nbextensions\", \"0.5.1\", \"\"),\n",
- " (\"rise\", \"5.5.1\", \"\"),\n",
- " (\"typing-extensions\", \"3.7.4\", \"typing_extensions\"),\n",
- " (\"idna-ssl\", \"1.1.0\", \"idna_ssl\"),\n",
- " (\"multidict\", \"4.5.2\", \"\"),\n",
- " (\"yarl\", \"1.3.0\", \"\"),\n",
- " (\"async-timeout\", \"3.0.1\", \"async_timeout\"),\n",
- " (\"aiohttp\", \"3.6.2\", \"\"),\n",
- " (\"simpervisor\", \"0.3\", \"\"),\n",
- " (\"jupyter_server\", \"0.1.1\", \"\"),\n",
- " (\"jupyter-server-proxy\", \"1.1.0\", \"jupyter_server_proxy\"),\n",
- " (\"jupyterlab_github\", \"1.0.1\", \"\"),\n",
- " (\"jupyterlab-gitlab\", \"0.3.0\", \"jupyterlab_gitlab\"),\n",
- " (\"jupyterlab-quickopen\", \"0.3.0\", \"jupyterlab_quickopen\"),\n",
- " (\"zstandard\", \"0.12.0\", \"\"),\n",
- " (\"itk_core\", \"5.0.1\", \"\"),\n",
- " (\"itk_filtering\", \"5.0.1\", \"\"),\n",
- " (\"itk_segmentation\",\"5.0.1\", \"\"),\n",
- " (\"itk_numerics\", \"5.0.1\", \"\"),\n",
- " (\"itk_registration\",\"5.0.1\", \"\"),\n",
- " (\"itk_io\", \"5.0.1\", \"\"),\n",
- " (\"itk-meshtopolydata\", \"0.5.1\", \"\"),\n",
- " (\"pyct\", \"0.4.6\", \"\"),\n",
- " (\"colorcet\", \"2.0.2\", \"\"),\n",
- " (\"itkwidgets\", \"0.22.0\", \"\"),\n",
- " (\"ujson\", \"1.35\", \"\"),\n",
- " (\"jupyterlab_iframe\", \"0.2.1\", \"\"),\n",
- " (\"python-dotenv\", \"0.10.3\", \"dotenv\"),\n",
- " (\"jupyterlab_latex\",\"1.0.0\", \"\"),\n",
- " (\"jupyterlab_slurm\",\"1.0.5\", \"\"),\n",
- " (\"jupyterlmod\", \"1.7.5\", \"\"),\n",
- " (\"nbresuse\", \"0.3.2\", \"\"),\n",
- " (\"colorama\", \"0.4.1\", \"\"),\n",
- " (\"nbdime\", \"1.1.0\", \"\"),\n",
- " (\"smmap2\", \"2.0.5\", \"smmap\"),\n",
- " (\"gitdb2\", \"2.0.6\", \"gitdb\"),\n",
- " (\"GitPython\", \"3.0.4\", \"git\"),\n",
- " (\"jupyterlab-git\", \"0.8.1\", \"jupyterlab_git\"),\n",
- " (\"sidecar\", \"0.3.0\", \"\"),\n",
- " (\"pycodestyle\", \"2.5.0\", \"\"),\n",
- " (\"autopep8\", \"1.4.4\", \"\"),\n",
- " (\"yapf\", \"0.28.0\", \"\"),\n",
- " (\"toml\", \"0.10.0\", \"\"),\n",
- " (\"pathspec\", \"0.6.0\", \"\"),\n",
- " (\"typed_ast\", \"1.4.0\", \"\"),\n",
- " (\"regex\", \"2019.11.1\",\"\"),\n",
- " (\"black\", \"19.3b0\", \"\"),\n",
- " (\"jupyterlab-code-formatter\", \"0.6.1\", \"jupyterlab_code_formatter\"),\n",
- " (\"pamela\", \"1.0.0\", \"\"),\n",
- " (\"certipy\", \"0.1.3\", \"\"),\n",
- " (\"oauthlib\", \"3.1.0\", \"\"),\n",
- " (\"jupyterhub\", \"1.0.0\", \"\"),\n",
- " (\"appmode\", \"0.6.0\", \"\"),\n",
- " (\"HeapDict\", \"1.0.1\", \"heapdict\"),\n",
- " (\"zict\", \"1.0.0\", \"\"),\n",
- " (\"tblib\", \"1.5.0\", \"\"),\n",
- " (\"sortedcontainers\",\"2.1.0\", \"\"),\n",
- " (\"msgpack\", \"0.6.2\", \"\"),\n",
- " (\"dask\", \"2.6.0\", \"\"),\n",
- " (\"distributed\", \"2.6.0\", \"\"),\n",
- " (\"dask-jobqueue\", \"0.7.0\", \"\"),\n",
- " (\"dask_labextension\", \"1.0.3\", \"\"),\n",
- " (\"Automat\", \"0.8.0\", \"automat\"),\n",
- " (\"PyHamcrest\", \"1.9.0\", \"hamcrest\"),\n",
- " (\"Twisted\", \"19.7.0\", \"twisted\"),\n",
- " (\"autobahn\", \"19.10.1\", \"\"),\n",
- " (\"constantly\", \"15.1.0\", \"\"),\n",
- " (\"hyperlink\", \"19.0.0\", \"\"),\n",
- " (\"incremental\", \"17.5.0\", \"\"),\n",
- " (\"txaio\", \"18.8.1\", \"\"),\n",
- " (\"zope.interface\", \"4.6.0\", \"\"),\n",
- " (\"backcall\", \"0.1.0\", \"\"),\n",
- " (\"wslink\", \"0.1.11\", \"\"),\n",
- " (\"jupyterlab_pygments\", \"0.1.0\",\"\"),\n",
- " (\"ipyvue\", \"1.0.0\", \"\"),\n",
- " (\"ipyvuetify\", \"1.1.1\", \"\"),\n",
- " (\"voila\", \"0.1.14\", \"\"),\n",
- " (\"voila-material\", \"0.2.5\", \"-\"),\n",
- " (\"voila-gridstack\", \"0.0.6\", \"-\"),\n",
- " (\"voila-vuetify\", \"0.1.1\", \"-\"), \n",
- " (\"dicom-upload\", \"v0.1.0\", \"\"),\n",
- " (\"fileupload\", \"master\", \"\"),\n",
- " (\"pvlink\", \"0.1.2\", \"\"),\n",
- " (\"julia\", \"0.5.0\", \"\"),\n",
- " (\"textwrap3\", \"0.9.2\", \"\"),\n",
- " (\"ansiwrap\", \"0.8.4\", \"\"),\n",
- " (\"backports.weakref\",\"1.0.post1\",\"\"),\n",
- " (\"backports.tempfile\",\"1.0\", \"\"),\n",
- " (\"tqdm\", \"4.41.0\", \"\"),\n",
- " (\"tenacity\", \"6.0.0\", \"\"),\n",
- " (\"papermill\", \"1.2.1\", \"\"),\n",
- " \n",
- " # PythonPackages for Tutorials\n",
- " (\"patsy\", \"0.5.1\", \"\"),\n",
- " (\"statsmodels\", \"0.10.2\", \"\"),\n",
- " (\"cftime\", \"1.0.4.2\", \"\"),\n",
- " (\"vega_datasets\", \"0.8.0\", \"\"),\n",
- " (\"arviz\", \"0.5.1\", \"\"),\n",
- " (\"Theano\", \"1.0.4\", \"\"),\n",
- " (\"altair\", \"3.3.0\", \"\"),\n",
- " (\"cssselect\", \"1.1.0\", \"\"),\n",
- " (\"smopy\", \"0.0.7\", \"\"),\n",
- " (\"joblib\", \"0.14.1\", \"\"),\n",
- " (\"scikit-learn\", \"0.22\", \"\"),\n",
- " (\"memory_profiler\", \"0.55.0\", \"\"),\n",
- " (\"h5py\", \"2.10.0\", \"\"),\n",
- " (\"line_profiler\", \"2.1.2\", \"\"),\n",
- " (\"pymc3\", \"3.8\", \"\"),\n",
- " (\"llvmlite\", \"0.30.0\", \"\"),\n",
- " (\"numba\", \"0.46.0\", \"\"),\n",
- " (\"numexpr\", \"2.7.0\", \"\"),\n",
- " (\"ipythonblocks\", \"1.9.0\", \"\"),\n",
- " (\"pydub\", \"0.23.1\", \"\"),\n",
- " (\"multipledispatch\",\"0.6.0\", \"\"),\n",
- " (\"partd\", \"1.1.0\", \"\"),\n",
- " (\"locket\", \"0.2.0\", \"\"),\n",
- " (\"fsspec\", \"0.6.2\", \"\"),\n",
- " (\"datashape\", \"0.5.2\", \"\"),\n",
- " (\"datashader\", \"0.9.0\", \"\"),\n",
- " (\"selenium\", \"3.141.0\", \"\"),\n",
- " (\"graphviz\", \"0.13.2\", \"\"),\n",
- " (\"vincent\", \"0.4.4\", \"\"),\n",
- " (\"Shapely\", \"1.6.4.post2\",\"\"),\n",
- " (\"pyshp\", \"2.1.0\", \"\"),\n",
- " (\"Cartopy\", \"0.17.0\", \"\"),\n",
- " (\"pandas-datareader\",\"0.8.1\", \"\"),\n",
- "]\n",
- "\n",
- "from pip._vendor import pkg_resources\n",
- "def get_version(package):\n",
- " package = package.lower()\n",
- " return next((p.version for p in pkg_resources.working_set if p.project_name.lower() == package), f\"{Fore.RED}NO MATCH{Style.RESET_ALL}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Run a Sanity Check\n",
- "A test of the Python packages follows here. \n",
- "\n",
- "Attention:\n",
- " - Slight changes in the version numbers are due to compatibility problems we encountered and therefore had to make adjustments.\n",
- " - \"NO MATCH\" - Not all package versions can be automatically found by this script\n",
- " - \"IMPORT FAILED\" - Import failures are possible if python packages are not ment to be importable by the developer."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "PYPI NAME : IMPORT NAME REQ.VERS.|INST.VERS. IMPORT TIME\n",
- "=================================================================================\n"
- ]
- },
- {
- "ename": "NameError",
- "evalue": "name 'pkg_list' 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-1-09245ef6496d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"PYPI NAME\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mljust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\": \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"IMPORT NAME\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mljust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"REQ.VERS.|INST.VERS.\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mljust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"IMPORT TIME\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"=================================================================================\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mpkg_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpkg_version\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpkg_importname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpkg_list\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mpkg_importname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mpkg_importname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpkg_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mNameError\u001b[0m: name 'pkg_list' is not defined"
- ]
- }
- ],
- "source": [
- "import importlib\n",
- "from colorama import Fore, Style\n",
- "from timeit import default_timer as timer\n",
- "\n",
- "print(\"PYPI NAME\".ljust(20), \": \", \"IMPORT NAME\".ljust(20) + \"REQ.VERS.|INST.VERS.\".ljust(25) + \"IMPORT TIME\")\n",
- "print(\"=================================================================================\")\n",
- "for pkg_name, pkg_version, pkg_importname in pkg_list:\n",
- " if not pkg_importname:\n",
- " pkg_importname = pkg_name\n",
- " pkg = None\n",
- " \n",
- " try:\n",
- " # import package\n",
- " start_time = timer()\n",
- " if pkg_importname != \"-\":\n",
- " pkg = importlib.import_module(pkg_importname)\n",
- " import_time = timer() - start_time\n",
- " \n",
- " # get version\n",
- " try:\n",
- " version = pkg.__version__\n",
- " if not isinstance(pkg.__version__, str):\n",
- " raise\n",
- " except:\n",
- " version = get_version(pkg_name)\n",
- " \n",
- " if version != pkg_version:\n",
- " version = pkg_version.ljust(10) + \" != \" + f\"{Fore.RED}\" + version.ljust(10) + f\"{Style.RESET_ALL}\"\n",
- "\n",
- " print(pkg_name.ljust(20), \": \", pkg_importname.ljust(20), version.ljust(24), f\"{import_time:.6f}\"+\"s\")\n",
- " except:\n",
- " print(pkg_name.ljust(20), \": \", pkg_importname.ljust(20), f\"{Fore.RED}IMPORT FAILED{Style.RESET_ALL}\") \n",
- " \n",
- " #try:\n",
- " # print(\"\".ljust(24), pkg.__file__)\n",
- " #except:\n",
- " # print(\"\".ljust(24), f\"{Fore.RED}UNKNOWN{Style.RESET_ALL}\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/001-Jupyter/Modify_JupyterKernel_at_NotebookRuntime.ipynb b/001-Jupyter/Modify_JupyterKernel_at_NotebookRuntime.ipynb
deleted file mode 100644
index 7dfc3edb91aca602b905bfc30742761c0f2cda9b..0000000000000000000000000000000000000000
--- a/001-Jupyter/Modify_JupyterKernel_at_NotebookRuntime.ipynb
+++ /dev/null
@@ -1,153 +0,0 @@
-{
- "cells": [
- {
- "attachments": {
- "f3736838-48a6-4450-9c91-5730ded4aca0.jpg": {
- "image/jpeg": 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"
- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "Author: [Jonathan Windgassen](mailto:j.windgassen@fz-juelich.de)\n",
- "------------------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# How to modify/extend a running Jupyter Kernel\n",
- "\n",
- "There are many cases where one needs modules from JupyterLab for a project. But building your own kernel is often a detour from the original idea or is annoying when publishing your project. \n",
- "By adding these 4 cells to the top of your project you can load modules for the project \"on the fly\".\n",
- "\n",
- "Besides that this also adds a ways of installing python packages via pip without disrupting the uses packages or access to the system site-packages\n",
- "\n",
- "-------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "- First we create to temp-folders in the */tmp* directory, who will contain the venv where we install the required packages and a folder that stores the PYTHONPATH and LD_LIBRARY_PATH environment variable. This is necessary because loading modules manipulates these variables but we can't access the changes from within python, so we store the changed variables in a folder.\n",
- "- Then we use a bash-shell to:\n",
- " - Load the Modules\n",
- " - Create a venv and installing ipykernel in there\n",
- " - Write PYTHONPATH and LD_LIBRARY_PATH to the tempdir\n",
- "- Beacause the Dynamic Linker of Python doesn't detect changes in LD_LIBRARY_PATH we need to reboot the Interpreter afterwards to carry these changes over. To gain access to the venv we will start Python from there.\n",
- "- After that we install the required modules.\n",
- "\n",
- "**Note**: The third cell **won't** show that it's completed and the Notebook will show `Python 3 | Starting` at the bottom, although the interpreter already reloaded compeltely. You can savely ignore this and continue with the third shell. As soon as this has finished the Notebook will show `Python3 | Idle` again."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os, sys, tempfile\n",
- "\n",
- "tempdir = tempfile.mkdtemp()\n",
- "venv_folder = tempfile.mkdtemp()\n",
- "print(tempdir, venv_folder)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%%bash -s \"$tempdir\" \"$venv_folder\" # Pass the paths to the bash-subshell\n",
- "\n",
- "######################################################################\n",
- "## The modules go here ##\n",
- "## We will use Stage/Devel-2020 with Python 3.8 as a example ##\n",
- "######################################################################\n",
- "\n",
- "# Update to Stage Devel-2020\n",
- "module --force purge\n",
- "module use $OTHERSTAGES \n",
- "module load Stages/Devel-2020\n",
- "\n",
- "module load GCC/9.3.0\n",
- "module load Python/3.8.5\n",
- "\n",
- "# Create a venv with the python from Devel-2020 and install ipykernel there (needed for communicating with Jupyter)\n",
- "# If you don't change Python above this should be a normal Python 3.6 venv\n",
- "python -m venv --system-site-packages $2\n",
- "source $2/bin/activate\n",
- "pip install --quiet ipykernel\n",
- "\n",
- "# Store the new variables to the temp-folder\n",
- "echo $PYTHONPATH > $1/pythonpath\n",
- "echo $LD_LIBRARY_PATH > $1/librarypath"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# The arguments for the new python interpreter. We need to initialize ipykernel or JupyterLab will fail to integrate the new process\n",
- "args = [f\"{venv_folder}/bin/python\", \"-m\", \"ipykernel\"]\n",
- "args.extend(sys.argv)\n",
- "\n",
- "# Because we call \"execve\" instead of \"execv\" we get the option to set the environment variables in the process. We use this to smuggle in the changed LD_LIBRARY_PATH and PYTHONPATH.\n",
- "# You also can pass the location of the 2 temp-folders as new environment variables if you want to delete them later for cleanup.\n",
- "env = {\"PYTHONPATH\": open(f\"{tempdir}/pythonpath\").read(),\n",
- " \"LD_LIBRARY_PATH\": open(f\"{tempdir}/librarypath\").read(),\n",
- " \"tempdir\": tempdir,\n",
- " \"venv_folder\": venv_folder}\n",
- " \n",
- "!echo Restarting Interpreter from $venv_folder/bin/python. Please execute the next cell\n",
- "os.execve(f\"{venv_folder}/bin/python\", args, env)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Because we are in the venv now, we can safely install all packages that we need and don't come with the Python3-Kernel. No need to add --user\n",
- "%pip install --quiet ..."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "-----\n",
- "\n",
- "After that you can import all you libraries (remember that the Interpreter restarted and you need to reimport os/sys/tempfile if you need them) and start with the notebook"
- ]
- }
- ],
- "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": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
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--- a/002-Methods/001-Computing/Howto_Dask_onJUWELS.ipynb
+++ /dev/null
@@ -1,346 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Dask Extension"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "This notebook will give you a short introduction into the Dask Extension on JUWELS. It allows you to run Jobs on the compute nodes, even if your JupyterLab is running interactively on the login node. \n",
- "First you have to define on which project and partition it should be running."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "queue = \"batch\" # batch, gpus, develgpus, etc.\n",
- "project = \"training2005\" # your project: zam, training19xx, etc.\n",
- "port = 56755"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Monte-Carlo Estimate of $\\pi$\n",
- "\n",
- "We want to estimate the number $\\pi$ using a [Monte-Carlo method](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods) exploiting that the area of a quarter circle of unit radius is $\\pi/4$ and that hence the probability of any randomly chosen point in a unit square to lie in a unit circle centerd at a corner of the unit square is $\\pi/4$ as well. So for N randomly chosen pairs $(x, y)$ with $x\\in[0, 1)$ and $y\\in[0, 1)$, we count the number $N_{circ}$ of pairs that also satisfy $(x^2 + y^2) < 1$ and estimage $\\pi \\approx 4 \\cdot N_{circ} / N$.\n",
- "\n",
- "[<img src=\"https://upload.wikimedia.org/wikipedia/commons/8/84/Pi_30K.gif\" \n",
- " width=\"50%\" \n",
- " align=top\n",
- " alt=\"PI monte-carlo estimate\">](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Core Lessons\n",
- "\n",
- "- setting up SLURM (and other jobqueue) clusters\n",
- "- Scaling clusters\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Set up a Slurm cluster\n",
- "\n",
- "We'll create a SLURM cluster and have a look at the job-script used to start workers on the HPC scheduler."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import dask\n",
- "from dask.distributed import Client\n",
- "from dask_jobqueue import SLURMCluster\n",
- "import os\n",
- "\n",
- "cluster = SLURMCluster(\n",
- " cores=96,\n",
- " processes=4,\n",
- " memory=\"70GB\",\n",
- " shebang=\"#!/usr/bin/env bash\",\n",
- " queue=queue,\n",
- " dashboard_address=\":\"+str(port),\n",
- " walltime=\"00:30:00\",\n",
- " local_directory=\"/tmp\",\n",
- " death_timeout=\"30s\",\n",
- " log_directory=f'{os.environ[\"HOME\"]}/dask_jobqueue_logs/',\n",
- " interface=\"ib1\",\n",
- " project=project,\n",
- " extra=['--host $SLURMD_NODENAME.ib.juwels.fzj.de'],\n",
- ")\n",
- "# optional: job_extra=[\"--reservation=reservation_name\"]\n",
- "# interface can be skipped if the master process runs on a comput node"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(cluster.job_script())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "cluster"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "client = Client(cluster)\n",
- "client"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## You can visit the Dask Dashboard at the following url: \n",
- "```\n",
- "https://jupyter-jsc.fz-juelich.de/user/<user_name>/<lab_name>/proxy/<port>/status\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## You can integrate it into your JupyterLab environment by putting the link into the Dask Extension"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "![\"Dask\"](https://zam10183.zam.kfa-juelich.de/hub/static/images/dask2.png \"dask\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Afterwards you can press on the orange buttons to open a new tab in your JupyterLab Environment."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Scale the cluster to two nodes\n",
- "\n",
- "A look at the Dashboard reveals that there are no workers in the clusetr. Let's start 4 workers (in 2 SLURM jobs).\n",
- "\n",
- "For the distiction between _workers_ and _jobs_, see [the Dask jobqueue docs](https://jobqueue.dask.org/en/latest/howitworks.html#workers-vs-jobs)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "cluster.scale(4) # scale to 4 _workers_"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## The Monte Carlo Method"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import dask.array as da\n",
- "import numpy as np\n",
- "\n",
- "\n",
- "def calc_pi_mc(size_in_bytes, chunksize_in_bytes=200e6):\n",
- " \"\"\"Calculate PI using a Monte Carlo estimate.\"\"\"\n",
- "\n",
- " size = int(size_in_bytes / 8)\n",
- " chunksize = int(chunksize_in_bytes / 8)\n",
- "\n",
- " xy = da.random.uniform(0, 1, size=(size / 2, 2), chunks=(chunksize / 2, 2))\n",
- "\n",
- " in_circle = (xy ** 2).sum(axis=-1) < 1\n",
- " pi = 4 * in_circle.mean()\n",
- "\n",
- " return pi\n",
- "\n",
- "\n",
- "def print_pi_stats(size, pi, time_delta, num_workers):\n",
- " \"\"\"Print pi, calculate offset from true value, and print some stats.\"\"\"\n",
- " print(\n",
- " f\"{size / 1e9} GB\\n\"\n",
- " f\"\\tMC pi: {pi : 13.11f}\"\n",
- " f\"\\tErr: {abs(pi - np.pi) : 10.3e}\\n\"\n",
- " f\"\\tWorkers: {num_workers}\"\n",
- " f\"\\t\\tTime: {time_delta : 7.3f}s\"\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## The actual calculations\n",
- "\n",
- "We loop over different volumes of double-precision random numbers and estimate $\\pi$ as described above."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from time import time, sleep"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for size in (1e9 * n for n in (1, 10, 100)):\n",
- "\n",
- " start = time()\n",
- " pi = calc_pi_mc(size).compute()\n",
- " elaps = time() - start\n",
- "\n",
- " print_pi_stats(\n",
- " size, pi, time_delta=elaps, num_workers=len(cluster.scheduler.workers)\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Is it running?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To check if something has been started for you just use the following command in a terminal: \n",
- "```\n",
- "squeue | grep ${USER}\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Scaling the Cluster to twice its size\n",
- "\n",
- "We increase the number of workers by 2 and the re-run the experiments."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "new_num_workers = 2 * len(cluster.scheduler.workers)\n",
- "\n",
- "print(f\"Scaling from {len(cluster.scheduler.workers)} to {new_num_workers} workers.\")\n",
- "\n",
- "cluster.scale(new_num_workers)\n",
- "\n",
- "sleep(10)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "client"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Re-run same experiments with doubled cluster"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for size in (1e9 * n for n in (1, 10, 100)):\n",
- "\n",
- " start = time()\n",
- " pi = calc_pi_mc(size).compute()\n",
- " elaps = time() - start\n",
- "\n",
- " print_pi_stats(\n",
- " size, pi, time_delta=elaps, num_workers=len(cluster.scheduler.workers)\n",
- " )"
- ]
- }
- ],
- "metadata": {
- "anaconda-cloud": {},
- "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": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/002-Methods/001-Computing/PyDeepLearningVersion.ipynb b/002-Methods/001-Computing/PyDeepLearningVersion.ipynb
deleted file mode 100644
index b6a66a5008614b1467c58a6c46ac2259b987f837..0000000000000000000000000000000000000000
--- a/002-Methods/001-Computing/PyDeepLearningVersion.ipynb
+++ /dev/null
@@ -1,610 +0,0 @@
-{
- "cells": [
- {
- "attachments": {
- "88c90614-07ea-4ccb-aab3-7b63dc27a603.png": {
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"
- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img src=attachment:88c90614-07ea-4ccb-aab3-7b63dc27a603.png width=400, height=41>\n",
- "\n",
- "Author: [Jonathan Windgassen](mailto:j.windgassen@fz-juelich.de)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Python Face Blurer\n",
- "\n",
- "This Script will scan any images in the *Images*-folder or any videos in the *Video*-folder for faces/persons using the [Faster RCNN Open Images V4 Neural Network](https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1) and apply a blur to all findings.\n",
- "\n",
- "\n",
- "\n",
- "#### Table of Contents:\n",
- "1. Initializing the Project\n",
- " - Creating the Folder Structure\n",
- " - Imports\n",
- " - Configuration\n",
- "2. Initializing the Blur\n",
- " - Functions for bluring objects\n",
- " - Functions for drawing boxes\n",
- "3. Loading the Classifier\n",
- " - Pulling the Classifier\n",
- " - Input and Output Parsing\n",
- "4. Fetching all Images and Video\n",
- "5. Run Classifier and Apply Blur\n",
- " - Run Classifier over every Image and Video\n",
- " - Save to Output folder\n",
- " - Compact Frames into a Video\n",
- "6. Cleanup\n",
- "\n",
- "-----------------------------------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Initializing the Project"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Creating the Folder Structure\n",
- "\n",
- "For Python to find the Images and Videos it needs the path to the folders where the Images are stored. By default the notebook expects the following folder structure, but you can change the paths below if you want:\n",
- "<pre>\n",
- "├── face_blurer.ipynb\n",
- "├── Images\n",
- "│ ├── Image.png\n",
- "│ └── ...\n",
- "├── Images_Out\n",
- "├── Videos\n",
- "│ ├── Video.mp4\n",
- "│ └── ...\n",
- "└── Videos_Out\n",
- "</pre>\n",
- "\n",
- "Usage:\n",
- "- **Images**: Put all Images here that you want to have blured. OpenCV can load almost all file formats, but for a complete list look [here](https://docs.opencv.org/3.4/d4/da8/group__imgcodecs.html#ga288b8b3da0892bd651fce07b3bbd3a56).\n",
- "- **Images_Out**: After processing the new Images will be placed here. The name of the processed file can be specified in the config with *FILENAME_AFTER_BLURRING*.\n",
- "- **Videos**: Same deal as with the Images. Put all Videos here that you want to have processed.\n",
- "- **Videos_Out**: All processed Videos will be placed here after applying the algorithm. The new name will be the same as for the Images.\n",
- "\n",
- "> Note: If you want to load the files from a different place in storage you can do so by editing the variable in the configuration. More on that below! "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Imports\n",
- "\n",
- "#### *Please use the PyDeepLearning*-kernel or the kernel from **kernel.ipynb** to run this\n",
- "\n",
- "Import everything we need for the whole operation.\n",
- "- The main package for the model will be tensorflow, which will internally use [TensorflowHub](https://tfhub.dev/) to download the network.\n",
- "- We will apply the blur using PIL, the Python Image Library that nativly gets delivered with python.\n",
- "- matplotlib will be used to display images in the notebook. Usefull if you want to see the results of the blurring directly."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#######################################\n",
- "# Disable GPU Usage for TensorFlow\n",
- "#######################################\n",
- "import os\n",
- "os.environ['CUDA_VISIBLE_DEVICES'] = '-1'\n",
- "\n",
- "# Main package for the ANN\n",
- "import tensorflow as tf\n",
- "import tensorflow_hub as hub\n",
- "\n",
- "# Opening Files and Bluring\n",
- "import cv2 as cv\n",
- "import numpy as np\n",
- "\n",
- "# Displaying the image\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "from time import time # For measuring the inference time.\n",
- "from os import listdir # Getting available files\n",
- "import re # Config\n",
- "import tempfile\n",
- "import shutil\n",
- "\n",
- "print(\"tensorflow version: \" + tf.__version__)\n",
- "print(\"OpenCV version: \" + cv.__version__)\n",
- "\n",
- "# Check available GPU devices.\n",
- "print(\"\\nNum GPUs Available: \", len(tf.config.experimental.list_physical_devices(\"GPU\")))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Configuration\n",
- "\n",
- "Here you can configure every aspect of the algorithm\n",
- "\n",
- "The most interesting option is probably *FILTER_OBJETCS*, where you can specify what object should be detected/blurred. You can see all available object classes in [this image](https://storage.googleapis.com/openimages/2018_04/bbox_labels_600_hierarchy_visualizer/circle.html) or take a direct look at the [Open Image Dataset](https://storage.googleapis.com/openimages/web/visualizer/index.html?). The filter is interpreted as a Regex, so you can also filter for multiple classes, eg. by chaining them together with `|` or using wildcard symbols like `.`. In most cases you'll probably want to use `Person` or `Human face`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "FILTER_OBJECTS = \"Person\" # Regex. What Object you want to detect. Everything else will be ignored.\n",
- "FILENAME_AFTER_BLURRING = \"{}_blured\" # {} replaces the original filename. Don't add the extension (.jpg, .mp3) here\n",
- "DRAW_BOXES = False\n",
- "BLUR_OBJECTS = True\n",
- "BLUR_INTENSITY = 20\n",
- "EXTEND_BOXES = 20 # Increases the size of the boxes by x pixels in each direction\n",
- "\n",
- "FORCE_IMAGE_SIZE = (None, None) # Leave at None for no changes. You are free to only resize one Dimension and leave the other\n",
- "FORCE_VIDEO_SIZE = (None, None) # Same as above\n",
- "CROP_VIDEO_LENGTH = None # Time in seconds after which videos will be cropped. Recommended for testing\n",
- "\n",
- "VIDEO_CACHE_FOLDER = None # Before we output the final video all frames will be saved as a .png-file in a folder. If you specify a folder here they won't be deleted afterwards. Leave None to use a tempdir.\n",
- "FORCE_VIDEO_FORMAT = None # Leave None for Original. You must not include the preceding \".\"\n",
- "VIDEO_BITRATE = None # String or Integer. Leave None for default\n",
- "ENCODER = \"h264\" # E.g. \"h264\", \"hevc\"(h265), \"asv1\" (AV1)\n",
- "\n",
- "# When paths are relative the origin will be the folder where this notebook is. You MUST put an / behind the folder paths\n",
- "IMAGE_INPUT_FOLDER = \"./Images/\"\n",
- "IMAGE_OUTPUT_FOLDER = \"./Images_Out/\"\n",
- "VIDEO_INPUT_FOLDER = \"./Videos/\"\n",
- "VIDEO_OUTPUT_FOLDER = \"./Videos_Out/\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "----------------------\n",
- "## Initializing the Blur"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Functions for bluring objects"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def blur_objects(image, boxes, names, confidence, min_score=0.1):\n",
- " \"\"\"\n",
- " Apply the results from the network to the image and blur the objects specified above.\n",
- " \n",
- " From the network return values we need to pass where the objects are, what each object is and how confident the network if about the detection.\n",
- " Since those properties are given in seperate lists, each identified object needs to be in the same index for every parameter.\n",
- " \n",
- " :param image: (numpy.ndarray) The image to be drawn on.\n",
- " :param boxes: (numpy.ndarray) An Array of shape (n, 4) with the 4 coordinates for each box. Cordinate must be [ymin, xmin, ymax, xmax]\n",
- " :param names: (numpy.ndarray) An Array of shape (n) with the name of each Object.\n",
- " :param confidence: (numpy.ndarray) An Array of shape (n) with the confidence for each Object.\n",
- " :param min_score: (optional, int) The required confidence for the box to be applied\n",
- " \"\"\"\n",
- " \n",
- " for i in range(boxes.shape[0]):\n",
- " is_confident = confidence[i] >= min_score\n",
- " is_in_filter = re.search(FILTER_OBJECTS, names[i]) is not None\n",
- " \n",
- " if is_confident and is_in_filter:\n",
- " blur_object(image, boxes[i])\n",
- "\n",
- "\n",
- "def blur_object(image, coordinates, intensity=BLUR_INTENSITY):\n",
- " height, width = image.shape[:2]\n",
- " (ymin, xmin, ymax, xmax) = coordinates\n",
- " \n",
- " (x, w, y, h) = (int(xmin * width), int(xmax * width), int(ymin * height), int(ymax * height))\n",
- " \n",
- " # Blur the area of the face\n",
- " sub_image = image[y:h, x:w]\n",
- " sub_image = cv.blur(sub_image, (intensity, intensity))\n",
- "\n",
- " # Apply blurred face back to image\n",
- " image[y:h, x:w] = sub_image"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Functions for drawing boxes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def draw_boxes(image, boxes, names, confidence, min_score=0.1):\n",
- " \"\"\"\n",
- " Apply the results from the network to the image and draw a box around each identified object.\n",
- " \n",
- " From the network return values we need to pass where the boxes are, what object each box represents and confident the network if about the detection.\n",
- " Since those properties are given in seperate lists, each identified object needs to be in the same index for every parameter.\n",
- " \n",
- " :param image: (numpy.ndarray) The image to be drawn on.\n",
- " :param boxes: (numpy.ndarray) An Array of shape (n, 4) with the 4 coordinates for each box. Cordinate must be [ymin, xmin, ymax, xmax]\n",
- " :param names: (numpy.ndarray) An Array of shape (n) with the name of each Object.\n",
- " :param confidence: (numpy.ndarray) An Array of shape (n) with the confidence for each Object.\n",
- " :param min_score: (optional, int) The required confidence for the box to be applied\n",
- " \"\"\"\n",
- " \n",
- " for i in range(boxes.shape[0]):\n",
- " is_confident = confidence[i] >= min_score\n",
- " is_in_filter = re.search(FILTER_OBJECTS, names[i]) is not None\n",
- " \n",
- " if is_confident and is_in_filter:\n",
- " display_str = f\"{names[i]}: {int(100 * confidence[i])}%\"\n",
- " draw_bounding_box_on_image(image, boxes[i], display_str)\n",
- "\n",
- "\n",
- "def draw_bounding_box_on_image(image, coordinates, label=\"\", color=(0, 0, 255), thickness=4):\n",
- " \"\"\"\n",
- " Adds a bounding box to an image.\n",
- " \n",
- " :param image: (np.ndarray) The Image to be drawn on.\n",
- " :param coordinates: (tuple) Coordinates of the Box: (ymin, xmin, ymax, xmax). Each coordinate must be between 0 and 1.\n",
- " :param color: (optional, str) 7-digit String representing the Color in the format of '#rrggbb'.\n",
- " :param thickness: (optional, int) How thick the box should be.\n",
- " \"\"\"\n",
- " # Draw the Box itself\n",
- " height, width = image.shape[:2]\n",
- " (left, top) = (int(coordinates[1] * width) - EXTEND_BOXES, int(coordinates[0] * height) - EXTEND_BOXES)\n",
- " (width, height) = (int(coordinates[3] * width) - left + 2 * EXTEND_BOXES, int(coordinates[2] * height) - top + 2 * EXTEND_BOXES)\n",
- " \n",
- " image = cv.rectangle(image, (left, top, width, height), color, thickness)\n",
- " \n",
- " # Calculate text specs\n",
- " (width, height), _ = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.6, 1)\n",
- " height += 10\n",
- " width += 10\n",
- " position = top if top < height else top - height # Move label to the inside if not enough space above the box\n",
- " \n",
- " # Draw Text\n",
- " rect = (left, position, width, height)\n",
- " image = cv.rectangle(image, rect, color, -1)\n",
- " image = cv.putText(image, label, (left + 5, position + height - 5), cv.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "--------------\n",
- "## Loading the Classifier"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Pulling the Classifier\n",
- "\n",
- "Here we use tensorflow_hub to Pull the ANN from the [Tensorflow Hub](https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1). Be aware that depending on your setup the loading can take a few minutes."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "module_handle = \"./model\"\n",
- "classifier = hub.load(module_handle).signatures['default']"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Input and Output Parsing\n",
- "\n",
- "These Functions will load an Image as an numpy.ndarray, which will then be passed to *run_detector*, were they will be converted to the right format and passed to the classifier"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def display_image(image):\n",
- " \"\"\"Displays the image to the screen.\"\"\"\n",
- " plt.figure(figsize=(20, 15))\n",
- " plt.grid(False)\n",
- " plt.axis(\"off\")\n",
- " \n",
- " plt.imshow(image)\n",
- "\n",
- "\n",
- "def open_image(path, display=False, resolution=FORCE_IMAGE_SIZE):\n",
- " \"\"\"\n",
- " Open and format an image from disk.\n",
- " \n",
- " Open a picture from file in the path variable. After if gets resized and converted to RGB if neccesary. If chosen if will also be displayed. \n",
- " \n",
- " :param path: (str) The path to the Picture.\n",
- " :param display: (boolean, optional) Whether you want the image displayed\n",
- " :param width: (tuple, optional) The desired resolution after rescaling.\n",
- " \"\"\"\n",
- " image = cv.imread(path, cv.IMREAD_COLOR)\n",
- " \n",
- " if (resolution != (None, None)):\n",
- " width = resolution[0] or image.shape[1]\n",
- " height = resolution[1] or image.shape[0]\n",
- " \n",
- " image = cv.resize(image, dsize=(width, height), interpolation=cv.INTER_CUBIC)\n",
- " \n",
- " if display:\n",
- " display_image(image)\n",
- " \n",
- " return image\n",
- "\n",
- "\n",
- "def run_detector(detector, image):\n",
- " \"\"\"\n",
- " Runs the given classifier on the given image. Only works with detectors that accept the same input format as the original one.\n",
- " \n",
- " \n",
- " :param detector: (A trackable Object) The Classifier.\n",
- " :param image: (np.ndarray) The Image.\n",
- " :return: (Dict) A Dictionary with the objects found by the Classifier.\n",
- " \"\"\"\n",
- " img = image.astype(np.float32)\n",
- " img = np.expand_dims(img, axis=0) # add required additional dimension. Same as img[np.newaxis, :], but better readable\n",
- " img /= 255 # Normalize\n",
- " \n",
- " result = detector(tf.convert_to_tensor(img))\n",
- " \n",
- " # Extract the numeric values\n",
- " result = {key: value.numpy() for key, value in result.items()}\n",
- " result[\"detection_class_entities\"] = [name.decode('ascii') for name in result[\"detection_class_entities\"]]\n",
- " \n",
- " return result"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "--------------\n",
- "## Fetching all Images and Videos"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "images = listdir(IMAGE_INPUT_FOLDER)\n",
- "videos = listdir(VIDEO_INPUT_FOLDER)\n",
- "\n",
- "# Remove hidden files (Starting with .) and convert to an tuple with (name, format)\n",
- "images = [[file.split(\".\")[0], file.split(\".\")[-1]] for file in images if not file.startswith(\".\")]\n",
- "videos = [[file.split(\".\")[0], file.split(\".\")[-1]] for file in videos if not file.startswith(\".\")]\n",
- "\n",
- "print(f\"Found {len(images)} Images\")\n",
- "print(f\"Found {len(videos)} Videos\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---------------------\n",
- "\n",
- "## Run Classifier and Blur"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Run Classifier on every Image"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for file in images:\n",
- " image = open_image(IMAGE_INPUT_FOLDER + file[0] + \".\" + file[1])\n",
- " start_time = time()\n",
- " \n",
- " #######################################\n",
- " # Code that failes\n",
- " #######################################\n",
- " result = run_detector(classifier, image)\n",
- " \n",
- " if BLUR_OBJECTS:\n",
- " blur_objects(image, result[\"detection_boxes\"], result[\"detection_class_entities\"], result[\"detection_scores\"])\n",
- " \n",
- " if DRAW_BOXES:\n",
- " draw_boxes(image, result[\"detection_boxes\"], result[\"detection_class_entities\"], result[\"detection_scores\"])\n",
- " \n",
- " cv.imwrite(IMAGE_OUTPUT_FOLDER + FILENAME_AFTER_BLURRING.format(file[0]) + \".\" + file[1], image)\n",
- " \n",
- " print(f\"Name: {file[0]}\\t Format: {file[1]}\\t Size: {image.shape[1]}x{image.shape[0]}\\t Time: {time() - start_time:.2f}s\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Run Classifier on every Video"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "cache_folder = VIDEO_CACHE_FOLDER if VIDEO_CACHE_FOLDER else tempfile.mkdtemp() + \"/\"\n",
- "print(f\"Using folder {cache_folder}\")\n",
- "\n",
- "for file in videos:\n",
- " video = cv.VideoCapture(VIDEO_INPUT_FOLDER + file[0] + \".\" + file[1])\n",
- " start_time = time()\n",
- " \n",
- " width = FORCE_VIDEO_SIZE[0] or int(video.get(cv.CAP_PROP_FRAME_WIDTH))\n",
- " height = FORCE_VIDEO_SIZE[1] or int(video.get(cv.CAP_PROP_FRAME_HEIGHT))\n",
- " \n",
- " fps = video.get(cv.CAP_PROP_FPS)\n",
- " resolution = (width, height)\n",
- " frame_count = video.get(cv.CAP_PROP_FRAME_COUNT)\n",
- " \n",
- " # Will be needed for ffmpeg\n",
- " file.append(fps)\n",
- "\n",
- " # Create output folder\n",
- " folderpath = f\"{cache_folder}{file[0]}-{file[1]}/\"\n",
- " !mkdir $folderpath\n",
- " file.append(folderpath)\n",
- " \n",
- " counter = 1\n",
- " length = frame_count if CROP_VIDEO_LENGTH is None else int(CROP_VIDEO_LENGTH * fps)\n",
- " while counter <= length:\n",
- " success, frame = video.read()\n",
- " \n",
- " # If no new Frame could be loaded (aka the video ended)\n",
- " if not success:\n",
- " break\n",
- " \n",
- " # Print progress\n",
- " if counter % int(length / 100) == 0:\n",
- " percentage = int(counter * 100 / length) + 1\n",
- " string = 'X' * percentage\n",
- " print(f\"{percentage}% [{string.ljust(100)}] {counter}/{int(length)}\", end=\"\\r\")\n",
- " \n",
- " frame = cv.cvtColor(frame, cv.COLOR_BGR2RGB)\n",
- " result = run_detector(classifier, frame)\n",
- " \n",
- " if BLUR_OBJECTS:\n",
- " blur_objects(frame, result[\"detection_boxes\"], result[\"detection_class_entities\"], result[\"detection_scores\"])\n",
- " \n",
- " if DRAW_BOXES:\n",
- " draw_boxes(frame, result[\"detection_boxes\"], result[\"detection_class_entities\"], result[\"detection_scores\"])\n",
- " \n",
- " # Resize Frames\n",
- " if (FORCE_VIDEO_SIZE != (None, None)):\n",
- " frame = cv.resize(frame, dsize=resolution, interpolation=cv.INTER_CUBIC)\n",
- " \n",
- " Save as png\n",
- " frame = cv.cvtColor(frame, cv.COLOR_RGB2BGR)\n",
- " name = f\"{folderpath}{str(counter).zfill(6)}.png\"\n",
- " cv.imwrite(name, frame, [cv.IMWRITE_PNG_COMPRESSION, 4])\n",
- " \n",
- " \n",
- " counter += 1\n",
- " \n",
- " video.release()\n",
- " \n",
- " print(f\"\\nName: {file[0]}\\t Format: {file[1]}\\t Size: {resolution}\\t FPS: {fps:.2f}\\t Duration: {(frame_count / fps):.1f}s\\t Time: {(time() - start_time):.2f}s\\n\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Compact Frames into a Video\n",
- "\n",
- "As a final step the single Frames will be compressed into a Video-File\n",
- "\n",
- "TODO: Add Audio"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for file in videos:\n",
- " \n",
- " print(file)\n",
- " \n",
- " input_files = f\"{file[3]}%06d.png\"\n",
- " output_filename = f\"{VIDEO_OUTPUT_FOLDER}{FILENAME_AFTER_BLURRING.format(file[0])}.\" + (file[1] if FORCE_VIDEO_FORMAT is None else FORCE_VIDEO_FORMAT)\n",
- " fps = file[2]\n",
- " \n",
- " options = \"\"\n",
- " if VIDEO_BITRATE is not None:\n",
- " options += f\" -b:v {VIDEO_BITRATE}\"\n",
- " \n",
- " !ffmpeg -i $input_files -framerate $fps -y $options -loglevel +info $output_filename"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Cleanup\n",
- "\n",
- "Delete all tempdirs we created\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "if not VIDEO_CACHE_FOLDER:\n",
- " shutil.rmtree(cache_folder)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "PyDeepLearning-1.0",
- "language": "python",
- "name": "pydeeplearning"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.5"
- },
- "toc-autonumbering": false,
- "toc-showcode": false,
- "toc-showmarkdowntxt": false
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/002-Methods/002-Data/Install_UserSoftware.ipynb b/002-Methods/002-Data/Install_UserSoftware.ipynb
deleted file mode 100644
index f3aab39ce93b670dc751ddd5071c3e823cb194ac..0000000000000000000000000000000000000000
--- a/002-Methods/002-Data/Install_UserSoftware.ipynb
+++ /dev/null
@@ -1,314 +0,0 @@
-{
- "cells": [
- {
- "attachments": {
- "67258d94-84e6-4a0c-ae8f-c74332ec082e.jpg": {
- "image/jpeg": 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"
- }
- },
- "cell_type": "markdown",
- "metadata": {
- "toc-hr-collapsed": false
- },
- "source": [
- "\n",
- "Author: [Jens Henrik Göbbert](mailto:j.goebbert@fz-juelich.de)\n",
- "------------------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "toc-hr-collapsed": false
- },
- "source": [
- "# How to install User Software using EasyBuild\n",
- "\n",
- "[EasyBuild](https://www.easybuild.io) is a software build and installation framework that allows you to manage (scientific) software on High Performance Computing (HPC) systems in an efficient way. \n",
- "All software modules you can load with `module load` on the supercomputers at the Jülich Supercomputing Centre (JSC) was installed using EasyBuild.\n",
- "\n",
- "There are multiple reasons for a build & installation framework like [EasyBuild](https://easybuild.io). \n",
- "And some reasons are not only convincing for HPC centers but also for HPC users: \n",
- "- fully automates software builds\n",
- "- allows for easily reproducing previous builds\n",
- "- keep the software build recipes/specifications simple and human-readable\n",
- "- supports co-existence of versions/builds via dedicated installation prefix and module files\n",
- "- automagic dependency resolution\n",
- "- ...\n",
- " \n",
- "<div class=\"alert alert-block alert-info\">\n",
- "<b>UserInstallations</b></br>\n",
- "allow you to easily install software for your own use or for everyone in your compute-time project.</br> \n",
- "</div>\n",
- "This notebook shows you how to make and load your own first software module.\n",
- "\n",
- "-------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Introduction\n",
- "\n",
- "Whenever you want to load a certain software module you call `module load <software>` . \n",
- "This command searches all paths in `$MODULEPATH` for a matching configuration file.\n",
- "\n",
- "In general you start loading first a software stage, then a compiler and after that a MPI. \n",
- "e.g. `module load Stages/2020 GCC/10.3.0 ParaStationMPI`\n",
- "\n",
- "These special modules (stages, compilers, MPIs) additionally extend the module paths in `$MODULEPATH` . \n",
- "It ensures that you can load further modules, which fit to the loaded stage, compiler and MPI.\n",
- "\n",
- "Now, if compatible private or project software modules are installed (by yourself or someone in your project) \n",
- "the module paths is **additionally extended** by the special modules (compilers, MPIs) with ... \n",
- "\n",
- "<div class=\"alert alert-block alert-info\">\n",
- "<b>User-specific module paths</b></br>\n",
- "<ul>\n",
- " <li>\\$HOME/easybuild</li>\n",
- " <li>\\$PROJECT/easybuild</li>\n",
- "</ul>\n",
- "</div>\n",
- "\n",
- "The hierarchy of preference is `$HOME/easybuild` highest, then `$PROJECT/easybuild`, then the system installation:"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### 1) Private Software Modules -> $HOME/easybuild\n",
- "The symlink directory `$HOME/easybuild` can exist if you have created it. It is not available by default. \n",
- "**ATTENTION:** \n",
- "It must be a link to a place outside of `$HOME`, e.g. to `$PROJECT/$USER/easybuild` because of disk-quota limitations in `$HOME` . \n",
- "\n",
- "#### 2) Project Software Modules -> $PROJECT/easybuild\n",
- "The directory `$PROJECT/easybuild` can exist if anyone of the project has created it. It is not available by default. \n",
- "**ATTENTION:** \n",
- "Unlike the environment variable `$HOME`, which is set at login, `$PROJECT` is **NOT** \n",
- "You must call `jutil env activate -p <projectname>` before or set it manually with `export PROJECT=<path>` . \n",
- "\n",
- "#### 3) System Software Modules -> /p/software/\\<systemname>\n",
- "Most software modules are of course installed in the system-wide module paths. \n",
- "This paths are searched for at last and therefore you are free to overwrite software modules with private or project software modules. \n",
- "\n",
- "<div class=\"alert alert-block alert-info\">\n",
- "<b>ATTENTION:</b></br>\n",
- "\\$HOME and \\$PROJECT are variables that can change depending on your current enviroment.</br> \n",
- "This can be a benefit as long as you are aware of it.\n",
- "</div>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "----------------------------\n",
- "\n",
- "## Preparing the Environment\n",
- "- Check/Create installation directories\n",
- "- Load appropriate modules"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "# INPUT NEEDED:\n",
- "export EBINST_TYPE=private # project\n",
- "\n",
- "# Ensure private over project installation\n",
- "if [ \"${EBINST_TYPE}\" == \"private\" ]; then\n",
- " export PREFER_USER=1\n",
- "fi"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "PROJECT=/p/project/ccstvs\n"
- ]
- }
- ],
- "source": [
- "# Check for environment variable $PROJECT\n",
- "\n",
- "# export PROJECT=/p/project/<projectname>\n",
- "# or\n",
- "# jutil env activate -p <projectname>\n",
- "if [ -z $PROJECT ]; then\n",
- " echo '$PROJECT not set.'\n",
- " echo 'Please set $PROJECT first.'\n",
- "else\n",
- " if [ ! -d \"$PROJECT\" ]; then \n",
- " echo \"ERROR: $PROJECT is not a valid directory\"\n",
- " else\n",
- " echo \"PROJECT=$PROJECT\"\n",
- " fi\n",
- "fi"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "$HOME/easybuild -> /p/project/ccstvs/goebbert1/easybuild\n"
- ]
- }
- ],
- "source": [
- "# Check/Create installation directories\n",
- "\n",
- "# private installation -> $HOME/easybuild\n",
- "if [ \"${EBINST_TYPE}\" == \"private\" ]; then\n",
- " if [ ! -d \"$HOME/easybuild\" ]; then\n",
- " mkdir -p $PROJECT/$USER/easybuild\n",
- " ln -s $PROJECT/$USER/easybuild $HOME/easybuild\n",
- " echo 'Directory created, Link set: $HOME/easybuild -> $PROJECT/$USER/easybuild'\n",
- " else\n",
- " if [ ! -L \"$HOME/easybuild\" ]; then\n",
- " echo 'ERROR: $HOME/easybuild is needs to be a _LINK_.'\n",
- " fi\n",
- " fi\n",
- " echo \"\\$HOME/easybuild -> $(readlink $HOME/easybuild)\"\n",
- "fi\n",
- "\n",
- "# project installation -> $PROJECT/easybuild\n",
- "if [ \"${EBINST_TYPE}\" == \"project\" ]; then\n",
- " if [ ! -d \"$PROJECT/easybuild\" ]; then\n",
- " mkdir -p $PROJECT/easybuild\n",
- " echo 'Directory created: $PROJECT/easybuild'\n",
- " fi\n",
- " echo \"\\$PROJECT/easybuild == $PROJECT/easybuild\"\n",
- "fi"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Preparing the environment for use of requested stage ( 2020 ).\n",
- " \n",
- "\u001b[01;33m\n",
- "Performing a personal installation. To do a project wide installation,\n",
- "set the $PROJECT environment variable (for example via jutil).\n",
- "\u001b[0m \n",
- "\u001b[01;32m** LOADING USERSPACE DEVELOPER CONFIGURATION **\u001b[0m\n",
- "\n",
- "Preparing the environment for software installation via EasyBuild into\n",
- "userspace leveraging stage 2020\n",
- "\n",
- " - Adding our license servers to LM_LICENSE_FILE\n",
- " - Giving priority to JSC custom Toolchains (EASYBUILD_INCLUDE_TOOLCHAINS)\n",
- " - Giving priority to JSC custom EasyBlocks (EASYBUILD_INCLUDE_EASYBLOCKS)\n",
- " - Giving priority to JSC custom easyconfigs (EASYBUILD_ROBOT)\n",
- " - Allowing searching of distribution easyconfigs (EASYBUILD_SEARCH_PATHS)\n",
- " - To keep module view clean, hiding some dependencies (EASYBUILD_HIDE_DEPS)\n",
- " - Using JSC EasyBuild hooks (EASYBUILD_HOOKS)\n",
- "\n",
- "\u001b[01;33m - Setting EASYBUILD_PARALLEL to 8\u001b[0m\n",
- "\u001b[01;33m - Setting EASYBUILD_OPTARCH to Intel:march=core-avx2\u001b[0m\n",
- "\n",
- "\u001b[01;33mNote: If you wish to submit software builds to slurm with the '--job'\n",
- "flag you will need to set environment variables to configure job submission,\n",
- "see\n",
- "https://slurm.schedmd.com/sbatch.html#lbAJ\n",
- "for details.\n",
- "\u001b[0m \n",
- " Preparing the environment for use of requested stage ( 2020 ).\n",
- " \n",
- "\u001b[01;33m\n",
- "Performing a personal installation. To do a project wide installation,\n",
- "set the $PROJECT environment variable (for example via jutil).\n",
- "\u001b[0m \n",
- "\u001b[01;32m** LOADING USERSPACE DEVELOPER CONFIGURATION **\u001b[0m\n",
- "\n",
- "Preparing the environment for software installation via EasyBuild into\n",
- "userspace leveraging stage 2020\n",
- "\n",
- " - Adding our license servers to LM_LICENSE_FILE\n",
- " - Giving priority to JSC custom Toolchains (EASYBUILD_INCLUDE_TOOLCHAINS)\n",
- " - Giving priority to JSC custom EasyBlocks (EASYBUILD_INCLUDE_EASYBLOCKS)\n",
- " - Giving priority to JSC custom easyconfigs (EASYBUILD_ROBOT)\n",
- " - Allowing searching of distribution easyconfigs (EASYBUILD_SEARCH_PATHS)\n",
- " - To keep module view clean, hiding some dependencies (EASYBUILD_HIDE_DEPS)\n",
- " - Using JSC EasyBuild hooks (EASYBUILD_HOOKS)\n",
- "\n",
- "\u001b[01;33m - Setting EASYBUILD_PARALLEL to 8\u001b[0m\n",
- "\u001b[01;33m - Setting EASYBUILD_OPTARCH to Intel:march=core-avx2\u001b[0m\n",
- "\n",
- "\u001b[01;33mNote: If you wish to submit software builds to slurm with the '--job'\n",
- "flag you will need to set environment variables to configure job submission,\n",
- "see\n",
- "https://slurm.schedmd.com/sbatch.html#lbAJ\n",
- "for details.\n",
- "\u001b[0m \n"
- ]
- }
- ],
- "source": [
- "# Load appropriate modules\n",
- "module load Stages/2020\n",
- "module load UserInstallations\n",
- "module update"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "-------------------\n",
- "\n",
- "## Additional Informations\n",
- "All toolchains are supported except the toolchain `SYSTEM`."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div class=\"alert alert-block alert-info\">\n",
- "<b>ATTENTION:</b> \\$PROJECT gets evaluated to fill \\$MODULEPATH</br> \n",
- "Therefore you <b>MUST</b> call \"module update\" everytime you change \\$PROJECT` after loading any modules.</br> \n",
- "If you forget the update \\$MODULEPATH will not be changed and still points to paths from your previous value of \\$PROJECT.</br> \n",
- "</div> "
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Bash",
- "language": "bash",
- "name": "bash"
- },
- "language_info": {
- "codemirror_mode": "shell",
- "file_extension": ".sh",
- "mimetype": "text/x-sh",
- "name": "bash"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/002-Methods/002-Data/README.md b/002-Methods/002-Data/README.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/.gitignore b/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/.gitignore
deleted file mode 100644
index 0b4378c4e6ec7ca7774362234933e31298e53299..0000000000000000000000000000000000000000
--- a/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/.gitignore
+++ /dev/null
@@ -1,6 +0,0 @@
-.ipynb_checkpoints/*
-CatalystEnabledSimulation/.ipynb_checkpoints/*
-CatalystEnabledSimulation/__pycache__/
-
-fullgrid*
-slice*
diff --git a/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/CatalystEnabledSimulation/README.md b/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/CatalystEnabledSimulation/README.md
deleted file mode 100644
index b4b71e3131ed4805d55f870960951cec57c4a09e..0000000000000000000000000000000000000000
--- a/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/CatalystEnabledSimulation/README.md
+++ /dev/null
@@ -1,2 +0,0 @@
-This example is based on https://github.com/Kitware/ParaView/tree/v5.8.0/Examples/Catalyst/PythonFullExample
-with only small changes to their code.
\ No newline at end of file
diff --git a/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/CatalystEnabledSimulation/coprocessor.py b/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/CatalystEnabledSimulation/coprocessor.py
deleted file mode 100644
index c8c5d0719c8c1187ed60eae8bf5b77a43150707c..0000000000000000000000000000000000000000
--- a/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/CatalystEnabledSimulation/coprocessor.py
+++ /dev/null
@@ -1,88 +0,0 @@
-coProcessor = None
-
-def initialize():
- global coProcessor
- import paraview
- from vtk import vtkParallelCore
- import paraview.servermanager as pvsm
- import vtk
- from mpi4py import MPI
- import os, sys
-
- paraview.options.batch = True
- paraview.options.symmetric = True
- from paraview.modules import vtkRemotingServerManager as CorePython
- from paraview.modules.vtkRemotingApplication import vtkInitializationHelper
-
- if not pvsm.vtkProcessModule.GetProcessModule():
- pvoptions = None
- if paraview.options.batch:
- pvoptions = CorePython.vtkPVOptions();
- pvoptions.SetProcessType(CorePython.vtkPVOptions.PVBATCH)
- if paraview.options.symmetric:
- pvoptions.SetSymmetricMPIMode(True)
- vtkInitializationHelper.Initialize(sys.executable, pvsm.vtkProcessModule.PROCESS_BATCH, pvoptions)
-
-
- # we need ParaView 4.2 since ParaView 4.1 doesn't properly wrap
- # vtkPVPythonCatalyst
- if pvsm.vtkSMProxyManager.GetVersionMajor() < 4 or (pvsm.vtkSMProxyManager.GetVersionMajor() == 4 and pvsm.vtkSMProxyManager.GetVersionMinor() < 2):
- print('Must use ParaView v4.2 or greater')
- sys.exit(0)
-
- import numpy
- from paraview.modules import vtkPVCatalyst as catalyst
- from paraview.modules import vtkPVPythonCatalyst as pythoncatalyst
- import paraview.simple
- import paraview.vtk as vtk
- from paraview.vtk.util import numpy_support
- paraview.options.batch = True
- paraview.options.symmetric = True
-
- coProcessor = catalyst.vtkCPProcessor()
- pm = paraview.servermanager.vtkProcessModule.GetProcessModule()
- from mpi4py import MPI
-
-def finalize():
- global coProcessor
- coProcessor.Finalize()
- # if we are running through Python we need to finalize extra stuff
- # to avoid memory leak messages.
- import sys, ntpath
- if ntpath.basename(sys.executable) == 'python':
- from paraview.modules.vtkRemotingApplication import vtkInitializationHelper
- vtkInitializationHelper.Finalize()
-
-def addscript(name):
- global coProcessor
- from paraview.modules import vtkPVPythonCatalyst as pythoncatalyst
- pipeline = pythoncatalyst.vtkCPPythonScriptPipeline()
- pipeline.Initialize(name)
- coProcessor.AddPipeline(pipeline)
-
-def coprocess(time, timeStep, grid, attributes):
- global coProcessor
- import vtk
- from paraview.modules import vtkPVCatalyst as catalyst
- import paraview
- from paraview.vtk.util import numpy_support
- dataDescription = catalyst.vtkCPDataDescription()
- dataDescription.SetTimeData(time, timeStep)
- dataDescription.AddInput("input")
-
- if coProcessor.RequestDataDescription(dataDescription):
- import fedatastructures
- imageData = vtk.vtkImageData()
- imageData.SetExtent(grid.XStartPoint, grid.XEndPoint, 0, grid.NumberOfYPoints-1, 0, grid.NumberOfZPoints-1)
- imageData.SetSpacing(grid.Spacing)
-
- velocity = numpy_support.numpy_to_vtk(attributes.Velocity)
- velocity.SetName("velocity")
- imageData.GetPointData().AddArray(velocity)
-
- pressure = numpy_support.numpy_to_vtk(attributes.Pressure)
- pressure.SetName("pressure")
- imageData.GetCellData().AddArray(pressure)
- dataDescription.GetInputDescriptionByName("input").SetGrid(imageData)
- dataDescription.GetInputDescriptionByName("input").SetWholeExtent(0, grid.NumberOfGlobalXPoints-1, 0, grid.NumberOfYPoints-1, 0, grid.NumberOfZPoints-1)
- coProcessor.CoProcess(dataDescription)
diff --git a/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/CatalystEnabledSimulation/cpscript.py b/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/CatalystEnabledSimulation/cpscript.py
deleted file mode 100644
index b115bf4b155132e389551358d1f14c9038039337..0000000000000000000000000000000000000000
--- a/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/CatalystEnabledSimulation/cpscript.py
+++ /dev/null
@@ -1,93 +0,0 @@
-from paraview.simple import *
-from paraview import coprocessing
-
-#--------------------------------------------------------------
-# Code generated from cpstate.py to create the CoProcessor.
-
-
-# ----------------------- CoProcessor definition -----------------------
-
-def CreateCoProcessor():
- def _CreatePipeline(coprocessor, datadescription):
- class Pipeline:
- filename_6_pvti = coprocessor.CreateProducer( datadescription, "input" )
-
- # create a new 'Parallel ImageData Writer'
- imageDataWriter1 = servermanager.writers.XMLPImageDataWriter(Input=filename_6_pvti)
-
- # register the writer with coprocessor
- # and provide it with information such as the filename to use,
- # how frequently to write the data, etc.
- coprocessor.RegisterWriter(imageDataWriter1, filename="fullgrid_%t.pvti", freq=10000)
-
- Slice1 = Slice( Input=filename_6_pvti, guiName="Slice1", Crinkleslice=0, SliceOffsetValues=[0.0], Triangulatetheslice=1, SliceType="Plane" )
- Slice1.SliceType.Offset = 0.0
- Slice1.SliceType.Origin = [9.0, 11.0, 9.0]
- Slice1.SliceType.Normal = [1.0, 0.0, 0.0]
-
- # create a new 'Parallel PolyData Writer'
- parallelPolyDataWriter1 = servermanager.writers.XMLPPolyDataWriter(Input=Slice1)
-
- # register the writer with coprocessor
- # and provide it with information such as the filename to use,
- # how frequently to write the data, etc.
- coprocessor.RegisterWriter(parallelPolyDataWriter1, filename='slice_%t.pvtp', freq=1000)
-
- return Pipeline()
-
- class CoProcessor(coprocessing.CoProcessor):
- def CreatePipeline(self, datadescription):
- self.Pipeline = _CreatePipeline(self, datadescription)
-
- coprocessor = CoProcessor()
- freqs = {'input': [10, 100]}
- coprocessor.SetUpdateFrequencies(freqs)
- return coprocessor
-
-#--------------------------------------------------------------
-# Global variables that will hold the pipeline for each timestep
-# Creating the CoProcessor object, doesn't actually create the ParaView pipeline.
-# It will be automatically setup when coprocessor.UpdateProducers() is called the
-# first time.
-coprocessor = CreateCoProcessor()
-
-#--------------------------------------------------------------
-# Enable Live-Visualizaton with ParaView
-coprocessor.EnableLiveVisualization(True, 1)
-
-
-# ---------------------- Data Selection method ----------------------
-
-def RequestDataDescription(datadescription):
- "Callback to populate the request for current timestep"
- global coprocessor
- if datadescription.GetForceOutput() == True:
- # We are just going to request all fields and meshes from the simulation
- # code/adaptor.
- for i in range(datadescription.GetNumberOfInputDescriptions()):
- datadescription.GetInputDescription(i).AllFieldsOn()
- datadescription.GetInputDescription(i).GenerateMeshOn()
- return
-
- # setup requests for all inputs based on the requirements of the
- # pipeline.
- coprocessor.LoadRequestedData(datadescription)
-
-# ------------------------ Processing method ------------------------
-
-def DoCoProcessing(datadescription):
- "Callback to do co-processing for current timestep"
- global coprocessor
-
- # Update the coprocessor by providing it the newly generated simulation data.
- # If the pipeline hasn't been setup yet, this will setup the pipeline.
- coprocessor.UpdateProducers(datadescription)
-
- # Write output data, if appropriate.
- coprocessor.WriteData(datadescription);
-
- # Write image capture (Last arg: rescale lookup table), if appropriate.
- coprocessor.WriteImages(datadescription, rescale_lookuptable=False)
-
- # Live Visualization, if enabled.
- coprocessor.DoLiveVisualization(datadescription, "localhost", 22222)
diff --git a/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/CatalystEnabledSimulation/fedatastructures.py b/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/CatalystEnabledSimulation/fedatastructures.py
deleted file mode 100644
index b4b2424dca04a236dfceb137373ab1fd035bdeae..0000000000000000000000000000000000000000
--- a/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/CatalystEnabledSimulation/fedatastructures.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import numpy
-
-class GridClass:
- """
- We are working with a uniform grid which will be
- represented as a vtkImageData in Catalyst. It is partitioned
- in the x-direction only.
- """
- def __init__(self, pointDimensions, spacing):
- from mpi4py import MPI
- comm = MPI.COMM_WORLD
- rank = comm.Get_rank()
- size = comm.Get_size()
-
- self.XStartPoint = int(pointDimensions[0]*rank/size)
- self.XEndPoint = int(pointDimensions[0]*(rank+1)/size)
- if rank+1 != size:
- self.XEndPoint += 1
- else:
- self.XEndPoint = pointDimensions[0]-1
- self.NumberOfYPoints = pointDimensions[1]
- self.NumberOfZPoints = pointDimensions[2]
- self.NumberOfGlobalXPoints = pointDimensions[0]
-
- self.PointDimensions = pointDimensions
- self.Spacing = spacing
-
- def GetNumberOfPoints(self):
- return (self.XEndPoint-self.XStartPoint+1)*self.PointDimensions[1]*self.PointDimensions[2]
-
- def GetNumberOfCells(self):
- return (self.XEndPoint-self.XStartPoint)*(self.PointDimensions[1]-1)*(self.PointDimensions[2]-1)
-
-class AttributesClass:
- """
- We have velocity point data and pressure cell data.
- """
- def __init__(self, grid):
- self.Grid = grid
-
- def Update(self, time):
- self.Velocity = numpy.zeros((self.Grid.GetNumberOfPoints(), 3))
- self.Velocity = self.Velocity + time
- self.Pressure = numpy.random.rand(self.Grid.GetNumberOfCells())
diff --git a/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/CatalystEnabledSimulation/fedriver.py b/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/CatalystEnabledSimulation/fedriver.py
deleted file mode 100644
index 929628c4345fbb1fcc509ef03ab96006c71663a5..0000000000000000000000000000000000000000
--- a/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/CatalystEnabledSimulation/fedriver.py
+++ /dev/null
@@ -1,47 +0,0 @@
-"""
-A simple example of a Python simulation code working with Catalyst.
-It depends on numpy and mpi4py being available. The environment
-variables need to be set up properly to find Catalyst when running directly
-from python. For Linux
-and Mac machines they should be:
-export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:<Catalyst build dir>/lib
-export PYTHONPATH=<Catalyst build dir>/lib:<Catalyst build dir>/lib/site-packages
-
-Alternatively, pvbatch or pvpython can be used which will automatically set up
-system paths for using Catalyst.
-
-When running, Catalyst scripts must be added in on the command line. For example:
-</path/to/pvpython> fedriver.py cpscript.py
-mpirun -np 4 </path/to/pvbatch> -sym fedriver.py cpscript.py
-"""
-from time import sleep
-
-import numpy
-import sys
-from mpi4py import MPI
-
-comm = MPI.COMM_WORLD
-rank = comm.Get_rank()
-
-import fedatastructures
-
-grid = fedatastructures.GridClass([10, 12, 10], [2, 2, 2])
-attributes = fedatastructures.AttributesClass(grid)
-doCoprocessing = True
-
-if doCoprocessing:
- import coprocessor
- coprocessor.initialize()
- for i in sys.argv[1:]:
- if rank == 0:
- print('Using Catalyst script', i)
- coprocessor.addscript(i)
-
-for i in range(10000):
- sleep(0.5)
- attributes.Update(i)
- if doCoprocessing:
- coprocessor.coprocess(i, i, grid, attributes)
-
-if doCoprocessing:
- coprocessor.finalize()
diff --git a/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/Catalyst_Example.ipynb b/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/Catalyst_Example.ipynb
deleted file mode 100644
index 1eb84fdac0094baae300bd5a2b057a730d8964e4..0000000000000000000000000000000000000000
--- a/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/Catalyst_Example.ipynb
+++ /dev/null
@@ -1,487 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Using Catalyst and ParaView in Jupyter Notebook"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This is the full Version, a less complex example can be seen in [Catalyst_Example_minimal](Catalyst_Example_minimal.ipynb).\n",
- "\n",
- "This is setting up all components visible in the grafik below. Therefore giving you a notebook, that can issue ParaView commands to the pvserver, that is connected to a small sample simulation. The visualisation will be done in the notebook and is visible in your webbrowser.\n",
- "\n",
- "<img src=\"img/Communication.png\" width=\"400\">"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Table of contents\n",
- "\n",
- "- Requirements \n",
- "- Start pvserver\n",
- "- Start Catalyst enabled simulation\n",
- "- Setup Render Window in Browser\n",
- "- Show logging output in Jupyter notebook\n",
- "- Establish Catalyst Connection\n",
- "- Setup render pipeline using ParaView Python Commands\n",
- "- Additional usefull functions for using Catalyst in Notebook"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Requirements\n",
- "- The bash variable JUPYTER_LOG_DIR needs to be set to a folder, where logs can be written. It can be set, before starting jupyter by executing `export JUPYTER_LOG_DIR=\"/your/path\"` before running `jupyter-lab`. The two following lines allows to check the value that was set when starting jupyter:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from os import environ\n",
- "print(environ.get('JUPYTER_LOG_DIR'))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "- Additional files are needed. They can be obtained by cloning the git repo, or copying the files in the folder `001-Catalyst2JupyterLab-Tutorial`. Afterwards the path to the folder needs to be written into the file `${JUPYTER_LAB_DIR}/catalystTutorialPath.txt`. This can be done with the following commands:\n",
- "\n",
- "```\n",
- "git init repo j4j_notebooks\n",
- "cd j4j_notebooks\n",
- "git remote add origin https://gitlab.version.fz-juelich.de/jupyter4jsc/j4j_notebooks.git\n",
- "git config core.sparseCheckout true\n",
- "echo \"002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/*\" >> .git/info/sparse-checkout\n",
- "git pull --depth=1 origin master\n",
- "cd 002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/\n",
- "pwd >${JUPYTER_LOG_DIR}/catalystTutorialPath.txt\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import glob\n",
- "file = open(environ.get('JUPYTER_LOG_DIR') + \"/catalystTutorialPath.txt\",\"r\")\n",
- "catalystTutorialPath = file.readline().rstrip()\n",
- "file.close()\n",
- "if len(catalystTutorialPath) < 30:\n",
- " import sys\n",
- " print(\"Path is short: '\" + catalystTutorialPath + \"' is it the right path?\", file=sys.stderr)\n",
- " \n",
- "if len(glob.glob(catalystTutorialPath+ \"/*\")) < 4:\n",
- " import sys\n",
- " print(\"Not enough files in '\" + catalystTutorialPath + \"' is it the right path?\", file=sys.stderr)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Start pvserver\n",
- "Here we will start the pvserver on the same node as the notebook. It is possible to start the pvserver somewhere else, but then that will have to be done outside the notebook. Or using slurm, but then it is not known when the pvserver will be started (and where)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%%script bash --bg --proc server_process\n",
- "export OMP_NUM_THREADS=1\n",
- "pvserver --server-port=11223 > ${JUPYTER_LOG_DIR}/pvserver.log 2>&1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Start Catalyst enabled simulation\n",
- "Here a small sample simulation is started, that will run on the same node, and send random pressure data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%%script bash --bg --proc catalyst_process\n",
- "export OMP_NUM_THREADS=1\n",
- "pvpython CatalystEnabledSimulation/fedriver.py CatalystEnabledSimulation/cpscript.py > ${JUPYTER_LOG_DIR}/simulation.log 2>&1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Setup Render Window in Browser\n",
- "- loading needed python modules\n",
- "- Get URL to access this jupyter Lab\n",
- "- Using pvlink connect with the websocket provided by ParaView. Here using a connection to a pvserver as well. Additional examples and info for pvlink can be found [here](https://gitlab.version.fz-juelich.de/jupyter4jsc/j4j_extras/pvlink/-/blob/master/examples/Examples.ipynb \"pvlink Examples\").\n",
- "- displaying the render window in a box, to get control over the size of the render window"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from paraview import simple\n",
- "from pvlink import RemoteRenderer\n",
- "from ipywidgets import Box\n",
- "from os import environ\n",
- "\n",
- "Jupyter_URL = \"jupyter-jsc.fz-juelich.de\" + environ.get('JUPYTERHUB_SERVICE_PREFIX', '/')\n",
- "renderer = RemoteRenderer(pvserverHost=\"localhost\", pvserverPort = 11223, baseURL=Jupyter_URL, useJupyterServerProxyHttps=True, disableExternalPort=True)\n",
- "\n",
- "Box(children=[renderer], layout={\"height\": \"800px\"})"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Setup a view, that will be used by ParaView, setting up parameters for smooth rendering in this setup (forcing the use of the pvserver to render, and not the client)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from pvlink.utility import SetRecommendedRenderSettings, ResetCamera\n",
- "\n",
- "# Cerate a view\n",
- "view = simple.FindViewOrCreate(\"test\", \"RenderView\")\n",
- "SetRecommendedRenderSettings(view)\n",
- "renderer.update_render()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Show logging output in Jupyter notebook\n",
- "\n",
- "Not all errors and warnings are visible in the notebook, by calling the ParaView functions. For example, opening a file, that is not avaiable (on the remote machine running pvserver), will not lead to an error on the call to the function. Only after the call to update/render the pipeline, accessing the file will be tried. An error/warning will then be noted in the log. In this example, we write logfiles for the simulation and pvserver, by simply redirecting their output into files. The output of the ParaView client can be enabled to write into a file using the following command. Afterwards we start a thread for each logfile we want to watch, that watches for changes and writes them as output to the last used Jupyter cell."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from paraview.servermanager import vtk\n",
- "vtk.vtkLogger.LogToFile((environ.get('JUPYTER_LOG_DIR') + \"/paraview_client.log\"), vtk.vtkLogger.TRUNCATE, vtk.vtkLogger.VERBOSITY_WARNING) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import tailer\n",
- "from threading import Thread\n",
- "\n",
- "def tail(fileName, printPrefix=\"\"):\n",
- " file = open(fileName, 'r')\n",
- " for line in tailer.follow(file):\n",
- " print(printPrefix + line)\n",
- "\n",
- "def tailInThread(fileName, printPrefix=\"\"):\n",
- " thread = Thread(target=tail, kwargs=dict(fileName=str(fileName), printPrefix=str(printPrefix)))\n",
- " thread.daemon=True\n",
- " thread.start()\n",
- "\n",
- "tailInThread(environ.get('JUPYTER_LOG_DIR') + \"/pvserver.log\", \"pvserver: \")\n",
- "tailInThread(environ.get('JUPYTER_LOG_DIR') + \"/paraview_client.log\", \"paraview client: \")\n",
- "tailInThread(environ.get('JUPYTER_LOG_DIR') + \"/simulation.log\", \"simulation: \")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Establish Catalyst Connection\n",
- "Create the Object handeling the connection, before extracting data from the Simulation. Waiting is necessary, Because extracting the data does not work, unless the simulation is connected and one update has been performed since updating"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "catalyst = simple.CatalystConnection()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# open port for catalyst connection\n",
- "catalyst.Start()\n",
- "catalyst.AddUpdateFunction(renderer.update_render)\n",
- "catalyst.BlockTillConnected();"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# wait till simulation connected\n",
- "catalyst.BlockTillConnected()\n",
- "# extract data from simulation\n",
- "# supplying a source name, that can be used to find the ParaView source.\n",
- "# In case of different named input, or multiple input ports, alows to choose the desired input, that should be extracted\n",
- "extract = catalyst.Extract(\"extract\")\n",
- "# block till there is an update for the simulation data\n",
- "catalyst.BlockTillNextUpdate()\n",
- "# display simulation data\n",
- "simple.SetActiveSource(extract)\n",
- "extractDisplay = simple.Show(extract, view)\n",
- "\n",
- "ResetCamera(view, renderer)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Connection has been established, and the simulation output is now visible above in the render window"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Setup render pipeline using ParaView Python Commands\n",
- "To get the wished visualisation we need to setup the ParaView pipeline, to tell it what it is supposed to do with the data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# set scalar coloring\n",
- "simple.ColorBy(extractDisplay, (\"CELLS\", \"pressure\"))\n",
- "\n",
- "# rescale color and/or opacity maps used to include current data range\n",
- "extractDisplay.RescaleTransferFunctionToDataRange(True, False)\n",
- "\n",
- "# show color bar/color legend\n",
- "extractDisplay.SetScalarBarVisibility(view, True)\n",
- "\n",
- "# get color transfer function/color map for 'pressure'\n",
- "pressureLUT = simple.GetColorTransferFunction(\"pressure\")\n",
- "pressureLUT.RescaleTransferFunction(0.0, 1.0)\n",
- "\n",
- "# get opacity transfer function/opacity map for 'pressure'\n",
- "pressurePWF = simple.GetOpacityTransferFunction(\"pressure\")\n",
- "pressurePWF.RescaleTransferFunction(0.0, 1.0)\n",
- "\n",
- "# change representation type, for example wireframe or volume rendering\n",
- "#extractDisplay.SetRepresentationType(\"Wireframe\")\n",
- "extractDisplay.SetRepresentationType(\"Volume\");"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Additional usefull functions for using Catalyst in Notebook\n",
- "it s possible to test for an establised connection yourself, allowing you to do other stuff while waiting for the connection, checking if it is still running..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(catalyst.IsConnected())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "see if the simulation is paused by catalyst right now"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(catalyst.IsPaused())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Check the last time step the simulation transmitted"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(catalyst.GetTimeStep())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Pause the simulation on the next time step.\n",
- "To prevent accidential activation, you need to uncomment it before using it."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#catalyst.SetPauseSimulation(True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let the simulation continue running\n",
- "To prevent accidential activation, you need to uncomment it before using it."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#from time import sleep\n",
- "#sleep(3)\n",
- "#catalyst.SetPauseSimulation(False)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Pause the Simulation on specified time step\n",
- "To prevent accidential activation, you need to uncomment it before using it."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#catalyst.BlockTillTimeStepAndPause(275)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To see all sources that are supplied by the simulation to ParaView, call this function"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#print(catalyst.GetCatalystSources())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Get help for one function, or see the list of all avaiable functions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "catalyst.IsPaused?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "help(catalyst)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "PyParaView-5.8",
- "language": "python",
- "name": "pyparaview-5.8"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/Catalyst_Example_minimal.ipynb b/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/Catalyst_Example_minimal.ipynb
deleted file mode 100644
index 6d76b151ce3fa187c656ab1575b87bbab5358526..0000000000000000000000000000000000000000
--- a/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/Catalyst_Example_minimal.ipynb
+++ /dev/null
@@ -1,273 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Using Catalyst and ParaView in Jupyter Notebook"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This is the minimal Version, just shortly showing, what you need in any case. A complete example can be seen in [Catalyst_Example](Catalyst_Example.ipynb).\n",
- "\n",
- "This is setting up all components visible in the grafik below. Therefore giving you a notebook, that can use ParaView commands and is connected to a small sample simulation. The visualisation will be done in the notebook and is visible in your webbrowser.\n",
- "\n",
- "<img src=\"img/Communication_minimal.png\" width=\"400\">"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Table of contents\n",
- "\n",
- "- Requirements \n",
- "- Start Catalyst enabled simulation\n",
- "- Setup Render Window in Browser\n",
- "- Establish Catalyst Connection\n",
- "- Setup render pipeline using ParaView Python Commands"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Requirements\n",
- "- The bash variable JUPYTER_LOG_DIR needs to be set to a folder, where logs can be written. It can be set, before starting jupyter by executing `export JUPYTER_LOG_DIR=\"/your/path\"` before running `jupyter-lab`. The two following lines allows to check the value that was set when starting jupyter:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from os import environ\n",
- "print(environ.get('JUPYTER_LOG_DIR'))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "- Additional files are needed. They can be obtained by cloning the git repo, or copying the files in the folder `001-Catalyst2JupyterLab-Tutorial`. Afterwards the path to the folder needs to be written into the file `${JUPYTER_LAB_DIR}/catalystTutorialPath.txt`. This can be done with the following commands:\n",
- "\n",
- "```\n",
- "git init repo j4j_notebooks\n",
- "cd j4j_notebooks\n",
- "git remote add origin https://gitlab.version.fz-juelich.de/jupyter4jsc/j4j_notebooks.git\n",
- "git config core.sparseCheckout true\n",
- "echo \"002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/*\" >> .git/info/sparse-checkout\n",
- "git pull --depth=1 origin master\n",
- "cd 002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/\n",
- "pwd >${JUPYTER_LOG_DIR}/catalystTutorialPath.txt\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import glob\n",
- "file = open(environ.get('JUPYTER_LOG_DIR') + \"/catalystTutorialPath.txt\",\"r\")\n",
- "catalystTutorialPath = file.readline().rstrip()\n",
- "file.close()\n",
- "if len(catalystTutorialPath) < 30:\n",
- " import sys\n",
- " print(\"Path is short: '\" + catalystTutorialPath + \"' is it the right path?\", file=sys.stderr)\n",
- " \n",
- "if len(glob.glob(catalystTutorialPath+ \"/*\")) < 4:\n",
- " import sys\n",
- " print(\"Not enough files in '\" + catalystTutorialPath + \"' is it the right path?\", file=sys.stderr)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Start Catalyst enabled simulation\n",
- "Here a small sample simulation is started, that will run on the same node, and send random pressure data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%%script bash --bg --proc catalyst_process\n",
- "cd `cat ${JUPYTER_LOG_DIR}/catalystTutorialPath.txt`\n",
- "export OMP_NUM_THREADS=1\n",
- "pvpython CatalystEnabledSimulation/fedriver.py CatalystEnabledSimulation/cpscript.py > ${JUPYTER_LOG_DIR}/simulation.log 2>&1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Setup Render Window in Browser\n",
- "- loading needed python modules\n",
- "- Get URL to access this jupyter Lab\n",
- "- Using pvlink connect with the websocket provided by ParaView. Additional examples and info for pvlink can be found [here](https://gitlab.version.fz-juelich.de/jupyter4jsc/j4j_extras/pvlink/-/blob/master/examples/Examples.ipynb \"pvlink Examples\").\n",
- "- displaying the render window in a box, to get control over the size of the render window"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from paraview import simple\n",
- "from pvlink import RemoteRenderer\n",
- "from ipywidgets import Box\n",
- "from os import environ\n",
- "\n",
- "Jupyter_URL = \"jupyter-jsc.fz-juelich.de\" + environ.get('JUPYTERHUB_SERVICE_PREFIX', '/')\n",
- "renderer = RemoteRenderer(baseURL=\"jupyter-jsc.fz-juelich.de\" + environ.get('JUPYTERHUB_SERVICE_PREFIX', '/'), useJupyterServerProxyHttps=True, disableExternalPort=True)\n",
- "\n",
- "Box(children=[renderer], layout={\"height\": \"800px\"})"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Setup a view, that will be used by ParaView, setting up parameters for smooth rendering in this setup."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from pvlink.utility import SetRecommendedRenderSettings, ResetCamera\n",
- "\n",
- "# Cerate a view\n",
- "view = simple.FindViewOrCreate(\"test\", \"RenderView\")\n",
- "SetRecommendedRenderSettings(view)\n",
- "renderer.update_render()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Establish Catalyst Connection\n",
- "Create the Object handeling the connection, before extracting data from the Simulation. Waiting is necessary, Because extracting the data does not work, unless the simulation is connected and one update has been performed since updating"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "catalyst = simple.CatalystConnection()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# open port for catalyst connection\n",
- "catalyst.Start()\n",
- "catalyst.AddUpdateFunction(renderer.update_render)\n",
- "catalyst.BlockTillConnected();"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# wait till simulation connected\n",
- "catalyst.BlockTillConnected()\n",
- "# extract data from simulation\n",
- "# supplying a source name, that can be used to find the ParaView source.\n",
- "# In case of different named input, or multiple input ports, alows to choose the desired input, that should be extracted\n",
- "extract = catalyst.Extract(\"extract\")\n",
- "# block till there is an update for the simulation data\n",
- "catalyst.BlockTillNextUpdate()\n",
- "# display simulation data\n",
- "simple.SetActiveSource(extract)\n",
- "extractDisplay = simple.Show(extract, view)\n",
- "\n",
- "ResetCamera(view, renderer)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Connection has been established, and the simulation output is now visible above in the render window"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Setup render pipeline using ParaView Python Commands\n",
- "To get the wished visualisation we need to setup the ParaView pipeline, to tell it what it is supposed to do with the data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# set scalar coloring\n",
- "simple.ColorBy(extractDisplay, (\"CELLS\", \"pressure\"))\n",
- "\n",
- "# rescale color and/or opacity maps used to include current data range\n",
- "extractDisplay.RescaleTransferFunctionToDataRange(True, False)\n",
- "\n",
- "# show color bar/color legend\n",
- "extractDisplay.SetScalarBarVisibility(view, True)\n",
- "\n",
- "# get color transfer function/color map for 'pressure'\n",
- "pressureLUT = simple.GetColorTransferFunction(\"pressure\")\n",
- "pressureLUT.RescaleTransferFunction(0.0, 1.0)\n",
- "\n",
- "# get opacity transfer function/opacity map for 'pressure'\n",
- "pressurePWF = simple.GetOpacityTransferFunction(\"pressure\")\n",
- "pressurePWF.RescaleTransferFunction(0.0, 1.0)\n",
- "\n",
- "# change representation type, for example wireframe or volume rendering\n",
- "#extractDisplay.SetRepresentationType(\"Wireframe\")\n",
- "extractDisplay.SetRepresentationType(\"Volume\");"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "PyParaView-5.8",
- "language": "python",
- "name": "pyparaview-5.8"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/img/Communication.drawio b/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/img/Communication.drawio
deleted file mode 100644
index 0ae1581a436f6819e3574f60162e38763e49ce49..0000000000000000000000000000000000000000
--- a/002-Methods/003-Visualization/001-InSitu/001-Catalyst2JupyterLab-Tutorial/img/Communication.drawio
+++ /dev/null
@@ -1 +0,0 @@
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deleted file mode 100644
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"
- }
- },
- "cell_type": "markdown",
- "metadata": {
- "toc-hr-collapsed": false
- },
- "source": [
- "\n",
- "Author: [Jens Henrik Göbbert](mailto:j.goebbert@fz-juelich.de)\n",
- "------------------------------------"
- ]
- },
- {
- "attachments": {
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"
- }
- },
- "cell_type": "markdown",
- "metadata": {
- "toc-hr-collapsed": false
- },
- "source": [
- "# Add Xpra Start-Menu Entry\n",
- "\n",
- "\n",
- "Remote Desktop refers to the remote access to the desktop of a computer. Application programs are executed on one computer (server) and displayed and operated on another computer (client).\n",
- "You can start your **Remote Desktop** from the launch panel of Jupyter-JSC by clicking on the **Xpra icon**.\n",
- "\n",
- "X Persistent Remote Applications (Xpra) is a Remote Desktop tool which runs X clients on a remote host and directs their display to the local machine without losing any state.\n",
- "It differs from standard \"X forwarding\" in that it allows disconnection and reconnection without disrupting the forwarded application.\n",
- "\n",
- "On the Xpra remote desktop you have a **Start Menu** in the upper left corner. \n",
- "If you want to add your **own menu entries** there, then follow the instructions here.\n",
- "\n",
- "-------------------------\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Create an application desktop file"
- ]
- },
- {
- "attachments": {
- "317f9f6d-03b5-4dce-a65c-6f52a35c31dc.png": {
- "image/png": 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"
- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# create applications directory (if not existent)\n",
- "USER_APPLICATION_DIR=${HOME}/.local/share/applications\n",
- "mkdir -p ${USER_APPLICATION_DIR}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Lets create an icon for `ExampleTool` which starts from `$HOME/bin/exampletool.sh`"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div class=\"alert alert-block alert-info\">\n",
- "<b>ATTENTION:</b>\n",
- "The standard JSC start menu in an Xpra session through Jupyter-JSC adds desktop entries with the **Categories=UserApplications** to its own submenu\n",
- "</div>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# create desktop file\n",
- "{ cat > ${USER_APPLICATION_DIR}/ExampleTool.desktop; } << 'EOF'\n",
- "[Desktop Entry]\n",
- "Name=ExampleTool\n",
- "GenericName=ExampleTool\n",
- "Comment=my great example tool\n",
- "Exec=xterm -hold -e '$HOME/bin/exampletool.sh'\n",
- "Terminal=false\n",
- "Type=Application\n",
- "Encoding=UTF-8\n",
- "Icon=application\n",
- "Categories=UserApplications;\n",
- "Keywords=userapplication;\n",
- "X-Desktop-File-Install-Version=0.23\n",
- "EOF"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Bash",
- "language": "bash",
- "name": "bash"
- },
- "language_info": {
- "codemirror_mode": "shell",
- "file_extension": ".sh",
- "mimetype": "text/x-sh",
- "name": "bash"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/002-Methods/003-Visualization/Install_your_UnrealEngine.ipynb b/002-Methods/003-Visualization/Install_your_UnrealEngine.ipynb
deleted file mode 100644
index d11a9706e2cd50a555e05cc43be2787f3ad93e10..0000000000000000000000000000000000000000
--- a/002-Methods/003-Visualization/Install_your_UnrealEngine.ipynb
+++ /dev/null
@@ -1,583 +0,0 @@
-{
- "cells": [
- {
- "attachments": {
- "67258d94-84e6-4a0c-ae8f-c74332ec082e.jpg": {
- "image/jpeg": 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- }
- },
- "cell_type": "markdown",
- "metadata": {
- "toc-hr-collapsed": false
- },
- "source": [
- "\n",
- "Author: [Jens Henrik Göbbert](mailto:j.goebbert@fz-juelich.de)\n",
- "------------------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "toc-hr-collapsed": false
- },
- "source": [
- "# Install your own Unreal Engine\n",
- "\n",
- "The Unreal Engine is a game engine developed by **Epic Games**, first showcased in the 1998 game Unreal. It has been seen adoption by many non-gaming projects. \n",
- "Written in **C++**, the Unreal Engine features a high degree of portability, supporting a wide range of platforms. \n",
- "\n",
- "It can be **downloaded for free**, with its source code available on a GitHub private repository. \n",
- "It is free to use for non-commercial projects. Epic allows for its use in commercial products based on a royalty model.\n",
- "\n",
- "https://www.unrealengine.com\n",
- "\n",
- "-------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Building your own Unreal Engine environment is a four step process\n",
- "1. Prepare Github and Epic accounts\n",
- "2. Download the UnrealEngine source\n",
- "3. Build UnrealEngine\n",
- "4. Setup environment and start scripts\n",
- "\n",
- "More: \n",
- "https://www.ue4community.wiki/legacy/building-on-linux-qr8t0si2 \n",
- "https://docs.unrealengine.com/en-US/Platforms/Linux/BeginnerLinuxDeveloper/SettingUpAnUnrealWorkflow/3/index.html"
- ]
- },
- {
- "attachments": {
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"
- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 1. Prepare Github and Epic accounts\n",
- "(more details: https://docs.unrealengine.com/en-US/GettingStarted/DownloadingUnrealEngine/index.html)\n",
- "\n",
- "### Create a Github + Epic account\n",
- "\n",
- "**Github**\n",
- "If you do not have a Github account yet, \n",
- "you must first create one here: https://github.com/join\n",
- "\n",
- "**Epic**\n",
- "If you do not have an Epic account yet, \n",
- "you must first create one here: https://www.unrealengine.com/login \n",
- "and verify it via the email which gets send to you.\n",
- "\n",
- "### Associated your GitHub with your Epic account\n",
- "(more details: https://www.unrealengine.com/en-US/ue4-on-github)\n",
- "\n",
- "After creating a **GitHub** and **Epic** account, \n",
- "sign into [UnrealEngine.com](https://unrealengine.com) with your verified Epic Games account.\n",
- "1. Hover over your username in the top-right corner, and select the **Personal** button from the drop-down menu.\n",
- "2. With your account dashboard open, select the **Connections** tab from the sidebar. \n",
- "3. After opening the Connections menu, select the **Accounts** tab, and then select the **Connect** button below the GitHub icon. \n",
- "\n",
- "\n",
- "\n",
- "---------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2. Download the Unreal Engine source"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* define same setting for the installation\n",
- "\n",
- "<div class=\"alert alert-block alert-danger\">\n",
- "<b>ATTENTION:</b>\n",
- "You MUST set GITHUB_USER and GITHUB_PASSWORD here.\n",
- "</div>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# INPUT NEEDED:\n",
- "# set the name of the github repository tag of the UnrealEngine source repository you want to download\n",
- "UNREAL_TAG=4.22.3-release\n",
- "\n",
- "# set the root install path where all different Unreal installations will have their own subdirectory\n",
- "UNREAL_ROOT=${PROJECT}/${USER}/UnrealEngine\n",
- "# and ensure root install path for Unreal exists\n",
- "mkdir -p ${UNREAL_ROOT}\n",
- "\n",
- "# set the installation path of this concrete Unreal Engine install\n",
- "UNREAL_HOME=${UNREAL_ROOT}/UnrealEngine-${UNREAL_TAG}\n",
- "\n",
- "# set the Github username, which is associated to your Epic account\n",
- "GITHUB_USER=<your Github user name> # <<<<<<<<<<<<<<<<<<<<<<<<<<< YOUR INPUT MANDETORY\n",
- "GITHUB_PASSWD=<your Github password> # <<<<<<<<<<<<<<<<<<<<<<<<<<< YOUR INPUT MANDETORY\n",
- "\n",
- "echo \"UNREAL_TAG = ${UNREAL_TAG}\" # double check\n",
- "echo \"UNREAL_ROOT = ${UNREAL_ROOT}\" # double check\n",
- "echo \"UNREAL_HOME = ${UNREAL_HOME}\" # double check\n",
- "echo \"GITHUB_USER = ${GITHUB_USER}\" # double check"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* download the source from Github to ${UNREAL_HOME}\n",
- "\n",
- "<div class=\"alert alert-block alert-info\">\n",
- "<b>ATTENTION:</b>\n",
- "This takes long (~10min).\n",
- "</div>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "######################################################################################\n",
- "# ATTENTION: register to Epic first and join their GitHub repo to be able to download\n",
- "######################################################################################\n",
- "\n",
- "# check if root directory exists\n",
- "if [ -z ${UNREAL_ROOT} ] || [ ! -d \"${UNREAL_ROOT}\" ]; then\n",
- " echo \"ERROR: please create directory ${UNREAL_ROOT}\"\n",
- "fi\n",
- "cd ${UNREAL_ROOT}\n",
- "\n",
- "# ensure download directory does not exist already \n",
- "if [ -d \"${UNREAL_ROOT}/UnrealEngine\" ]; then\n",
- " echo \"ERROR: ${UNREAL_ROOT}/UnrealEngine exists. Please remove/rename is as it is needed for git clone\"\n",
- "else\n",
- "\n",
- "# Clone UnrealEngine repo with a github-account linked to an epic-account\n",
- "# ATTENTION: this can take some time (up to 15 min) ... be patient\n",
- "# example output:\n",
- "# remote: Enumerating objects: 111, done. \n",
- "# remote: Counting objects: 100% (111/111), done. \n",
- "# remote: Compressing objects: 100% (111/111), done. \n",
- "# remote: Total 1309563 (delta 0), reused 107 (delta 0), pack-reused 1309452 \n",
- "# Receiving objects: 100% (1309563/1309563), 1.77 GiB | 31.45 MiB/s, done.\n",
- "# Resolving deltas: 100% (827619/827619), done.\n",
- "# Note: checking out '375ba9730e72bf85b383c07a5e4a7ba98774bcb9'. \n",
- "# <text>\n",
- "# Checking out files: 100% (118708/118708), done.\n",
- " echo \"Start download at $(date)\"\n",
- " git clone -b ${UNREAL_TAG} --single-branch https://${GITHUB_USER}:${GITHUB_PASSWD}@github.com/EpicGames/UnrealEngine.git\n",
- " echo \"Finished download at $(date)\"\n",
- "fi"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* at least 4.22.3 does not support first directory of the build/install path to be a single character\n",
- " * better we patch it as this is the case at JSC with \"/p/\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "patch -p0 --ignore-whitespace << 'EOF'\n",
- "diff -Nru UnrealEngine/Engine/Source/Runtime/Core/Private/Misc/Paths.cpp UnrealEngine.orig/Engine/Source/Runtime/Core/Private/Misc/Paths.cpp\n",
- "--- UnrealEngine/Engine/Source/Runtime/Core/Private/Misc/Paths.cpp 2020-04-29 11:28:46.000000000 +0200\n",
- "+++ UnrealEngine.orig/Engine/Source/Runtime/Core/Private/Misc/Paths.cpp 2020-04-29 12:54:18.627019714 +0200\n",
- "@@ -1074,6 +1074,7 @@\n",
- " TArray<FString> SourceArray;\n",
- " Source.ParseIntoArray(SourceArray, TEXT(\"/\"), true);\n",
- "\n",
- "+#if PLATFORM_WINDOWS || PLATFORM_XBOXONE\n",
- " if (TargetArray.Num() && SourceArray.Num())\n",
- " {\n",
- " // Check for being on different drives\n",
- "@@ -1086,6 +1087,7 @@\n",
- " }\n",
- " }\n",
- " }\n",
- "+#endif\n",
- "\n",
- " while (TargetArray.Num() && SourceArray.Num() && TargetArray[0] == SourceArray[0])\n",
- " {\n",
- "EOF"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* we do not install a /usr/bin/python on our machines, but a /usr/bin/python2\n",
- " * replace /usr/bin/python -> /usr/bin/python2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# attention:\n",
- "# 1. we need to consider spaces in filenames: \"-d '\\n'\"\n",
- "# 2. we must not do the replace, if /usr/bin/python2 is already present: look ahead with perl \"(?!2)\" - this needs perl no sed\n",
- "find UnrealEngine/ -name '*.py' -type f \\\n",
- " -o -name '*.rc' -type f \\\n",
- " -o -name '*.cs' -type f \\\n",
- " -o -name '*.txt' -type f \\\n",
- " | xargs -d '\\n' perl -pi -e 's#/usr/bin/python(?!2)#/usr/bin/python2#g'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* rename directory to ${UNREAL_HOME}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# ensure installation directory does not already exists\n",
- "if [ -z ${UNREAL_HOME} ] || [ -d \"${UNREAL_HOME}\" ]; then\n",
- " echo \"ERROR: ${UNREAL_HOME} already exists. Please remove/rename directory ${UNREAL_HOME}\"\n",
- "fi\n",
- "\n",
- "# rename directory to match tag name\n",
- "mv UnrealEngine ${UNREAL_HOME}\n",
- "\n",
- "echo \"Unreal Engine source installed to ${UNREAL_HOME}\" # double check"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "----------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 3. Build UnrealEngine"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* prepare environment\n",
- " * change directory to source dir ${UNREAL_HOME}\n",
- " * load modules"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "cd ${UNREAL_HOME}\n",
- "module purge\n",
- "module use $OTHERSTAGES\n",
- "\n",
- "module load Stages/Devel-2020\n",
- "module load GCCcore/.9.3.0\n",
- "module load Python/3.8.5\n",
- "\n",
- "module load libtool/.2.4.6\n",
- "module load xdg-user-dirs/0.17\n",
- "module load GTK+/3.24.17\n",
- "module load OpenGL/2020\n",
- "\n",
- "module list"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* download required dependencies of build-in toolchain \n",
- "\n",
- "<div class=\"alert alert-block alert-info\">\n",
- "<b>ATTENTION:</b>\n",
- "This takes long (~10min).\n",
- "</div>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "echo \"Start Setup.sh at $(date)\"\n",
- "./Setup.sh\n",
- "echo \"Finished Setup.sh at $(date)\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* Setup Mono\n",
- "* Build UnrealBuildTool (written in C#)\n",
- "* Generate systemspecific Unreal project files with \n",
- "\n",
- "<div class=\"alert alert-block alert-info\">\n",
- "<b>ATTENTION:</b>\n",
- "This takes long (~5min).\n",
- "</div>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "echo \"Start GenerateProjectFiles.sh at $(date)\"\n",
- "./GenerateProjectFiles.sh\n",
- "echo \"Finished GenerateProjectFiles.sh at $(date)\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* Build all \n",
- "\n",
- "<div class=\"alert alert-block alert-info\">\n",
- "<b>ATTENTION:</b>\n",
- "This takes long (~30min).\n",
- "</div>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "echo \"Start make at $(date)\"\n",
- "make\n",
- "echo \"Finished make at $(date)\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* Sanity check"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "if [ -f \"${UNREAL_HOME}/Engine/Binaries/Linux/UE4Editor\" ]; then\n",
- " echo \"Sanity check SUCCESSFULL\"\n",
- "else\n",
- " echo \"Sanity check FAILED\"\n",
- "fi"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 4. Setup environment and start scripts\n",
- "\n",
- "<div class=\"alert alert-block alert-info\">\n",
- "<b>ATTENTION:</b>\n",
- "!!! ONLY start UE4Editor in a prepared environment - created by the script below !!!\n",
- "</div>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To run any UnrealEngine application or editor you need to prepare your environment first:\n",
- "1. load the same modules as you did at build time\n",
- "2. use VirtualGL to be able to use the hardware acceleration with 'vglrun'\n",
- "3. ensure all binaries and libs are automatically found\n",
- "4. start it and have fun"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "{ cat > ${UNREAL_ROOT}/run_UE4Editor-${UNREAL_TAG}.sh; } << 'EOF'\n",
- "#!/bin/bash\n",
- "\n",
- "# 1. load the same modules as you did at build time\n",
- "module purge\n",
- "module use $OTHERSTAGES\n",
- "module load Stages/Devel-2020\n",
- "module load GCCcore/.9.3.0\n",
- "module load Python/3.8.5\n",
- "module load libtool/.2.4.6\n",
- "module load xdg-user-dirs/0.17\n",
- "module load GTK+/3.24.17\n",
- "module load OpenGL/2020\n",
- "\n",
- "# 3. ensure all binaries and libs are automatically found\n",
- "export UNREAL_ROOT=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\n",
- "export UNREAL_HOME=${UNREAL_ROOT}/UnrealEngine-UNREAL_TAG\n",
- "export PATH=${UNREAL_HOME}/Engine/Binaries/Linux/:${PATH}\n",
- "export LD_LIBRARY_PATH=${UNREAL_HOME}/Engine/Binaries/Linux/:${LD_LIBRARY_PATH}\n",
- "\n",
- "# check if we can run vglrun (gpu=nvida + Xserver at display 0)\n",
- "# - an Nvidia GPU must be installed\n",
- "# - /tmp/.X11-unix/X0 must exist (owned by root and readable for you)\n",
- "# both should be the case on the visualization login nodes of JUWELS and all login nodes of JURECA-DC\n",
- "NVGPU=$(lspci -nnk | grep -i nvidia | grep -i \"in use\" | cut -d ' ' -f 5)\n",
- "if [ -S /tmp/.X11-unix/X0 ] && [ \"${NVGPU}\" == \"nvidia\" ]; then\n",
- " # ok, GPU found and probably accessible through VirtualGL\n",
- " echo >&2 \" -- hardware accelerated\"\n",
- " \n",
- " # 3. use VirtualGL to be able to use the hardware acceleration with 'vglrun'\n",
- " module load VirtualGL\n",
- " \n",
- " # 4. start it and have fun\n",
- " vglrun +v -d :0 UE4Editor -opengl4\n",
- "\n",
- "else\n",
- " # oh no, we need to warn the user\n",
- " echo >&2 \" -- software rendering\"\n",
- " echo >&2 \"!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\"\n",
- " echo >&2 \"!!! NO GPU FOUND - THIS WILL BE SLOW !!!\"\n",
- " echo >&2 \"!!! Try to run Unreal on nodes with GPU - it needs it !!!\"\n",
- " echo >&2 \"!!! For example JUWELS-VIS and JURECA-DC login nodes !!!\"\n",
- " echo >&2 \"!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\"\n",
- " # 4. start it and have fun\n",
- " UE4Editor -opengl4\n",
- "fi\n",
- "EOF\n",
- "\n",
- "sed -i 's/UNREAL_TAG/'\"${UNREAL_TAG}\"'/g' ${UNREAL_ROOT}/run_UE4Editor-${UNREAL_TAG}.sh\n",
- "chmod +x ${UNREAL_ROOT}/run_UE4Editor-${UNREAL_TAG}.sh\n",
- "\n",
- "echo \"Startscript created as ${UNREAL_ROOT}/run_UE4Editor-${UNREAL_TAG}.sh\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div class=\"alert alert-block alert-danger\">\n",
- "<b>ATTENTION:</b><br>\n",
- "<section>\n",
- " 1. You NEED to call \"UE4Editor\" in a remote desktop like it is provide through\n",
- " <ul>\n",
- " <li>Jupyter-JSC (https://jupyter-jsc.fz-juelich.de)<br></li>\n",
- " <li>and its Xpra on the JupyterLab-Launcher-Panel</li>\n",
- " </ul>\n",
- "</section>\n",
- "2. The first time you start your Unreal Engine takes long (~10min) because it needs to build some shaders - be patient!\n",
- "</div>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 5. Extras"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### add a menu entry to the start menu of the Xpra desktop \n",
- "Howto create your own UnrealEngine entry to the start menu is defined for Linux by FreeDesktop.org \n",
- "https://specifications.freedesktop.org/menu-spec/menu-spec-1.0.html\n",
- " \n",
- "<div class=\"alert alert-block alert-info\">\n",
- "<b>ATTENTION:</b>\n",
- "The standard JSC start menu in an Xpra session through Jupyter-JSC adds desktop entries with the Categories=UserApplications to its own submenu\n",
- "</div>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# create applications directory (if not existent)\n",
- "XDG_APPLICATION_DIR=${HOME}/.local/share/applications\n",
- "mkdir -p ${XDG_APPLICATION_DIR}\n",
- "\n",
- "# create desktop file\n",
- "{ cat > ${XDG_APPLICATION_DIR}/UE4Editor-${UNREAL_TAG}.desktop; } << 'EOF'\n",
- "[Desktop Entry]\n",
- "Name=UE4EDITOR_NAME\n",
- "GenericName=UE4EDITOR_NAME\n",
- "Comment=3D development environment UE4EDITOR_NAME\n",
- "Exec=xterm -hold -e 'UE4EDITOR_STARTSCRIPT'\n",
- "Terminal=false\n",
- "Type=Application\n",
- "Encoding=UTF-8\n",
- "Icon=unrealengine\n",
- "Categories=UserApplications;\n",
- "Keywords=visualization;\n",
- "X-Desktop-File-Install-Version=0.23\n",
- "EOF\n",
- "\n",
- "sed -i 's#UE4EDITOR_STARTSCRIPT#'\"${UNREAL_ROOT}/run_UE4Editor-${UNREAL_TAG}.sh\"'#g' ${XDG_APPLICATION_DIR}/UE4Editor-${UNREAL_TAG}.desktop\n",
- "sed -i 's#UE4EDITOR_NAME#'\"UE4Editor-${UNREAL_TAG}\"'#g' ${XDG_APPLICATION_DIR}/UE4Editor-${UNREAL_TAG}.desktop"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Bash",
- "language": "bash",
- "name": "bash"
- },
- "language_info": {
- "codemirror_mode": "shell",
- "file_extension": ".sh",
- "mimetype": "text/x-sh",
- "name": "bash"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/002-Methods/003-Visualization/README.md b/002-Methods/003-Visualization/README.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/002-Methods/004-Dashboards/001-Dash/001-Getting_Started/getting_started.ipynb b/002-Methods/004-Dashboards/001-Dash/001-Getting_Started/getting_started.ipynb
deleted file mode 100644
index 031c2153ae037c81c720c241d443e845c721cc25..0000000000000000000000000000000000000000
--- a/002-Methods/004-Dashboards/001-Dash/001-Getting_Started/getting_started.ipynb
+++ /dev/null
@@ -1,340 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# JupyterDash\n",
- "The `jupyter-dash` package makes it easy to develop Plotly Dash apps from the Jupyter Notebook and JupyterLab.\n",
- "\n",
- "Just replace the standard `dash.Dash` class with the `jupyter_dash.JupyterDash` subclass."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "from jupyter_dash import JupyterDash"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import dash\n",
- "import dash_core_components as dcc\n",
- "import dash_html_components as html\n",
- "import pandas as pd"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "When running in JupyterHub or Binder, call the `infer_jupyter_config` function to detect the proxy configuration."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "JupyterDash.infer_jupyter_proxy_config()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Load and preprocess data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "df = pd.read_csv('https://plotly.github.io/datasets/country_indicators.csv')\n",
- "available_indicators = df['Indicator Name'].unique()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Construct the app and callbacks"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\n",
- "\n",
- "app = JupyterDash(__name__, external_stylesheets=external_stylesheets)\n",
- "\n",
- "# Create server variable with Flask server object for use with gunicorn\n",
- "server = app.server\n",
- "\n",
- "app.layout = html.Div([\n",
- " html.Div([\n",
- "\n",
- " html.Div([\n",
- " dcc.Dropdown(\n",
- " id='crossfilter-xaxis-column',\n",
- " options=[{'label': i, 'value': i} for i in available_indicators],\n",
- " value='Fertility rate, total (births per woman)'\n",
- " ),\n",
- " dcc.RadioItems(\n",
- " id='crossfilter-xaxis-type',\n",
- " options=[{'label': i, 'value': i} for i in ['Linear', 'Log']],\n",
- " value='Linear',\n",
- " labelStyle={'display': 'inline-block'}\n",
- " )\n",
- " ],\n",
- " style={'width': '49%', 'display': 'inline-block'}),\n",
- "\n",
- " html.Div([\n",
- " dcc.Dropdown(\n",
- " id='crossfilter-yaxis-column',\n",
- " options=[{'label': i, 'value': i} for i in available_indicators],\n",
- " value='Life expectancy at birth, total (years)'\n",
- " ),\n",
- " dcc.RadioItems(\n",
- " id='crossfilter-yaxis-type',\n",
- " options=[{'label': i, 'value': i} for i in ['Linear', 'Log']],\n",
- " value='Linear',\n",
- " labelStyle={'display': 'inline-block'}\n",
- " )\n",
- " ], style={'width': '49%', 'float': 'right', 'display': 'inline-block'})\n",
- " ], style={\n",
- " 'borderBottom': 'thin lightgrey solid',\n",
- " 'backgroundColor': 'rgb(250, 250, 250)',\n",
- " 'padding': '10px 5px'\n",
- " }),\n",
- "\n",
- " html.Div([\n",
- " dcc.Graph(\n",
- " id='crossfilter-indicator-scatter',\n",
- " hoverData={'points': [{'customdata': 'Japan'}]}\n",
- " )\n",
- " ], style={'width': '49%', 'display': 'inline-block', 'padding': '0 20'}),\n",
- " html.Div([\n",
- " dcc.Graph(id='x-time-series'),\n",
- " dcc.Graph(id='y-time-series'),\n",
- " ], style={'display': 'inline-block', 'width': '49%'}),\n",
- "\n",
- " html.Div(dcc.Slider(\n",
- " id='crossfilter-year--slider',\n",
- " min=df['Year'].min(),\n",
- " max=df['Year'].max(),\n",
- " value=df['Year'].max(),\n",
- " marks={str(year): str(year) for year in df['Year'].unique()},\n",
- " step=None\n",
- " ), style={'width': '49%', 'padding': '0px 20px 20px 20px'})\n",
- "])\n",
- "\n",
- "\n",
- "@app.callback(\n",
- " dash.dependencies.Output('crossfilter-indicator-scatter', 'figure'),\n",
- " [dash.dependencies.Input('crossfilter-xaxis-column', 'value'),\n",
- " dash.dependencies.Input('crossfilter-yaxis-column', 'value'),\n",
- " dash.dependencies.Input('crossfilter-xaxis-type', 'value'),\n",
- " dash.dependencies.Input('crossfilter-yaxis-type', 'value'),\n",
- " dash.dependencies.Input('crossfilter-year--slider', 'value')])\n",
- "def update_graph(xaxis_column_name, yaxis_column_name,\n",
- " xaxis_type, yaxis_type,\n",
- " year_value):\n",
- " dff = df[df['Year'] == year_value]\n",
- "\n",
- " return {\n",
- " 'data': [dict(\n",
- " x=dff[dff['Indicator Name'] == xaxis_column_name]['Value'],\n",
- " y=dff[dff['Indicator Name'] == yaxis_column_name]['Value'],\n",
- " text=dff[dff['Indicator Name'] == yaxis_column_name]['Country Name'],\n",
- " customdata=dff[dff['Indicator Name'] == yaxis_column_name]['Country Name'],\n",
- " mode='markers',\n",
- " marker={\n",
- " 'size': 25,\n",
- " 'opacity': 0.7,\n",
- " 'color': 'orange',\n",
- " 'line': {'width': 2, 'color': 'purple'}\n",
- " }\n",
- " )],\n",
- " 'layout': dict(\n",
- " xaxis={\n",
- " 'title': xaxis_column_name,\n",
- " 'type': 'linear' if xaxis_type == 'Linear' else 'log'\n",
- " },\n",
- " yaxis={\n",
- " 'title': yaxis_column_name,\n",
- " 'type': 'linear' if yaxis_type == 'Linear' else 'log'\n",
- " },\n",
- " margin={'l': 40, 'b': 30, 't': 10, 'r': 0},\n",
- " height=450,\n",
- " hovermode='closest'\n",
- " )\n",
- " }\n",
- "\n",
- "\n",
- "def create_time_series(dff, axis_type, title):\n",
- " return {\n",
- " 'data': [dict(\n",
- " x=dff['Year'],\n",
- " y=dff['Value'],\n",
- " mode='lines+markers'\n",
- " )],\n",
- " 'layout': {\n",
- " 'height': 225,\n",
- " 'margin': {'l': 20, 'b': 30, 'r': 10, 't': 10},\n",
- " 'annotations': [{\n",
- " 'x': 0, 'y': 0.85, 'xanchor': 'left', 'yanchor': 'bottom',\n",
- " 'xref': 'paper', 'yref': 'paper', 'showarrow': False,\n",
- " 'align': 'left', 'bgcolor': 'rgba(255, 255, 255, 0.5)',\n",
- " 'text': title\n",
- " }],\n",
- " 'yaxis': {'type': 'linear' if axis_type == 'Linear' else 'log'},\n",
- " 'xaxis': {'showgrid': False}\n",
- " }\n",
- " }\n",
- "\n",
- "\n",
- "@app.callback(\n",
- " dash.dependencies.Output('x-time-series', 'figure'),\n",
- " [dash.dependencies.Input('crossfilter-indicator-scatter', 'hoverData'),\n",
- " dash.dependencies.Input('crossfilter-xaxis-column', 'value'),\n",
- " dash.dependencies.Input('crossfilter-xaxis-type', 'value')])\n",
- "def update_y_timeseries(hoverData, xaxis_column_name, axis_type):\n",
- " country_name = hoverData['points'][0]['customdata']\n",
- " dff = df[df['Country Name'] == country_name]\n",
- " dff = dff[dff['Indicator Name'] == xaxis_column_name]\n",
- " title = '<b>{}</b><br>{}'.format(country_name, xaxis_column_name)\n",
- " return create_time_series(dff, axis_type, title)\n",
- "\n",
- "\n",
- "@app.callback(\n",
- " dash.dependencies.Output('y-time-series', 'figure'),\n",
- " [dash.dependencies.Input('crossfilter-indicator-scatter', 'hoverData'),\n",
- " dash.dependencies.Input('crossfilter-yaxis-column', 'value'),\n",
- " dash.dependencies.Input('crossfilter-yaxis-type', 'value')])\n",
- "def update_x_timeseries(hoverData, yaxis_column_name, axis_type):\n",
- " dff = df[df['Country Name'] == hoverData['points'][0]['customdata']]\n",
- " dff = dff[dff['Indicator Name'] == yaxis_column_name]\n",
- " return create_time_series(dff, axis_type, yaxis_column_name)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Serve the app using `run_server`. Unlike the standard `Dash.run_server` method, the `JupyterDash.run_server` method doesn't block execution of the notebook. It serves the app in a background thread, making it possible to run other notebook calculations while the app is running.\n",
- "\n",
- "This makes it possible to iterativly update the app without rerunning the potentially expensive data processing steps."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dash app running on https://jupyter-jsc.fz-juelich.de/user/j.goebbert@fz-juelich.de/jureca_login/proxy/8050/\n"
- ]
- }
- ],
- "source": [
- "app.run_server()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "By default, `run_server` displays a URL that you can click on to open the app in a browser tab. The `mode` argument to `run_server` can be used to change this behavior. Setting `mode=\"inline\"` will display the app directly in the notebook output cell."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- " <iframe\n",
- " width=\"800\"\n",
- " height=\"650\"\n",
- " src=\"https://jupyter-jsc.fz-juelich.de/user/j.goebbert@fz-juelich.de/jureca_login/proxy/8050/\"\n",
- " frameborder=\"0\"\n",
- " allowfullscreen\n",
- " ></iframe>\n",
- " "
- ],
- "text/plain": [
- "<IPython.lib.display.IFrame at 0x7f664a42d198>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "app.run_server(mode=\"inline\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "When running in JupyterLab, with the `jupyterlab-dash` extension, setting `mode=\"jupyterlab\"` will open the app in a tab in JupyterLab.\n",
- "\n",
- "```python\n",
- "app.run_server(mode=\"jupyterlab\")\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "jupytext": {
- "formats": "ipynb,py:percent"
- },
- "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": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/002-Methods/004-Dashboards/001-Dash/001-Getting_Started/getting_started.py b/002-Methods/004-Dashboards/001-Dash/001-Getting_Started/getting_started.py
deleted file mode 100644
index 0e178a869d43e7670553bdb25ac78fd00ce58c96..0000000000000000000000000000000000000000
--- a/002-Methods/004-Dashboards/001-Dash/001-Getting_Started/getting_started.py
+++ /dev/null
@@ -1,223 +0,0 @@
-# ---
-# jupyter:
-# jupytext:
-# formats: ipynb,py:percent
-# text_representation:
-# extension: .py
-# format_name: percent
-# format_version: '1.3'
-# jupytext_version: 1.4.2
-# kernelspec:
-# display_name: Python 3
-# language: python
-# name: python3
-# ---
-
-# %% [markdown]
-# # JupyterDash
-# The `jupyter-dash` package makes it easy to develop Plotly Dash apps from the Jupyter Notebook and JupyterLab.
-#
-# Just replace the standard `dash.Dash` class with the `jupyter_dash.JupyterDash` subclass.
-
-# %%
-from jupyter_dash import JupyterDash
-
-# %%
-import dash
-import dash_core_components as dcc
-import dash_html_components as html
-import pandas as pd
-
-# %% [markdown]
-# When running in JupyterHub or Binder, call the `infer_jupyter_config` function to detect the proxy configuration.
-
-# %%
-JupyterDash.infer_jupyter_proxy_config()
-
-# %% [markdown]
-# Load and preprocess data
-
-# %%
-df = pd.read_csv('https://plotly.github.io/datasets/country_indicators.csv')
-available_indicators = df['Indicator Name'].unique()
-
-# %% [markdown]
-# Construct the app and callbacks
-
-# %%
-external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
-
-app = JupyterDash(__name__, external_stylesheets=external_stylesheets)
-
-# Create server variable with Flask server object for use with gunicorn
-server = app.server
-
-app.layout = html.Div([
- html.Div([
-
- html.Div([
- dcc.Dropdown(
- id='crossfilter-xaxis-column',
- options=[{'label': i, 'value': i} for i in available_indicators],
- value='Fertility rate, total (births per woman)'
- ),
- dcc.RadioItems(
- id='crossfilter-xaxis-type',
- options=[{'label': i, 'value': i} for i in ['Linear', 'Log']],
- value='Linear',
- labelStyle={'display': 'inline-block'}
- )
- ],
- style={'width': '49%', 'display': 'inline-block'}),
-
- html.Div([
- dcc.Dropdown(
- id='crossfilter-yaxis-column',
- options=[{'label': i, 'value': i} for i in available_indicators],
- value='Life expectancy at birth, total (years)'
- ),
- dcc.RadioItems(
- id='crossfilter-yaxis-type',
- options=[{'label': i, 'value': i} for i in ['Linear', 'Log']],
- value='Linear',
- labelStyle={'display': 'inline-block'}
- )
- ], style={'width': '49%', 'float': 'right', 'display': 'inline-block'})
- ], style={
- 'borderBottom': 'thin lightgrey solid',
- 'backgroundColor': 'rgb(250, 250, 250)',
- 'padding': '10px 5px'
- }),
-
- html.Div([
- dcc.Graph(
- id='crossfilter-indicator-scatter',
- hoverData={'points': [{'customdata': 'Japan'}]}
- )
- ], style={'width': '49%', 'display': 'inline-block', 'padding': '0 20'}),
- html.Div([
- dcc.Graph(id='x-time-series'),
- dcc.Graph(id='y-time-series'),
- ], style={'display': 'inline-block', 'width': '49%'}),
-
- html.Div(dcc.Slider(
- id='crossfilter-year--slider',
- min=df['Year'].min(),
- max=df['Year'].max(),
- value=df['Year'].max(),
- marks={str(year): str(year) for year in df['Year'].unique()},
- step=None
- ), style={'width': '49%', 'padding': '0px 20px 20px 20px'})
-])
-
-
-@app.callback(
- dash.dependencies.Output('crossfilter-indicator-scatter', 'figure'),
- [dash.dependencies.Input('crossfilter-xaxis-column', 'value'),
- dash.dependencies.Input('crossfilter-yaxis-column', 'value'),
- dash.dependencies.Input('crossfilter-xaxis-type', 'value'),
- dash.dependencies.Input('crossfilter-yaxis-type', 'value'),
- dash.dependencies.Input('crossfilter-year--slider', 'value')])
-def update_graph(xaxis_column_name, yaxis_column_name,
- xaxis_type, yaxis_type,
- year_value):
- dff = df[df['Year'] == year_value]
-
- return {
- 'data': [dict(
- x=dff[dff['Indicator Name'] == xaxis_column_name]['Value'],
- y=dff[dff['Indicator Name'] == yaxis_column_name]['Value'],
- text=dff[dff['Indicator Name'] == yaxis_column_name]['Country Name'],
- customdata=dff[dff['Indicator Name'] == yaxis_column_name]['Country Name'],
- mode='markers',
- marker={
- 'size': 25,
- 'opacity': 0.7,
- 'color': 'orange',
- 'line': {'width': 2, 'color': 'purple'}
- }
- )],
- 'layout': dict(
- xaxis={
- 'title': xaxis_column_name,
- 'type': 'linear' if xaxis_type == 'Linear' else 'log'
- },
- yaxis={
- 'title': yaxis_column_name,
- 'type': 'linear' if yaxis_type == 'Linear' else 'log'
- },
- margin={'l': 40, 'b': 30, 't': 10, 'r': 0},
- height=450,
- hovermode='closest'
- )
- }
-
-
-def create_time_series(dff, axis_type, title):
- return {
- 'data': [dict(
- x=dff['Year'],
- y=dff['Value'],
- mode='lines+markers'
- )],
- 'layout': {
- 'height': 225,
- 'margin': {'l': 20, 'b': 30, 'r': 10, 't': 10},
- 'annotations': [{
- 'x': 0, 'y': 0.85, 'xanchor': 'left', 'yanchor': 'bottom',
- 'xref': 'paper', 'yref': 'paper', 'showarrow': False,
- 'align': 'left', 'bgcolor': 'rgba(255, 255, 255, 0.5)',
- 'text': title
- }],
- 'yaxis': {'type': 'linear' if axis_type == 'Linear' else 'log'},
- 'xaxis': {'showgrid': False}
- }
- }
-
-
-@app.callback(
- dash.dependencies.Output('x-time-series', 'figure'),
- [dash.dependencies.Input('crossfilter-indicator-scatter', 'hoverData'),
- dash.dependencies.Input('crossfilter-xaxis-column', 'value'),
- dash.dependencies.Input('crossfilter-xaxis-type', 'value')])
-def update_y_timeseries(hoverData, xaxis_column_name, axis_type):
- country_name = hoverData['points'][0]['customdata']
- dff = df[df['Country Name'] == country_name]
- dff = dff[dff['Indicator Name'] == xaxis_column_name]
- title = '<b>{}</b><br>{}'.format(country_name, xaxis_column_name)
- return create_time_series(dff, axis_type, title)
-
-
-@app.callback(
- dash.dependencies.Output('y-time-series', 'figure'),
- [dash.dependencies.Input('crossfilter-indicator-scatter', 'hoverData'),
- dash.dependencies.Input('crossfilter-yaxis-column', 'value'),
- dash.dependencies.Input('crossfilter-yaxis-type', 'value')])
-def update_x_timeseries(hoverData, yaxis_column_name, axis_type):
- dff = df[df['Country Name'] == hoverData['points'][0]['customdata']]
- dff = dff[dff['Indicator Name'] == yaxis_column_name]
- return create_time_series(dff, axis_type, yaxis_column_name)
-
-
-# %% [markdown]
-# Serve the app using `run_server`. Unlike the standard `Dash.run_server` method, the `JupyterDash.run_server` method doesn't block execution of the notebook. It serves the app in a background thread, making it possible to run other notebook calculations while the app is running.
-#
-# This makes it possible to iterativly update the app without rerunning the potentially expensive data processing steps.
-
-# %%
-app.run_server()
-
-# %% [markdown]
-# By default, `run_server` displays a URL that you can click on to open the app in a browser tab. The `mode` argument to `run_server` can be used to change this behavior. Setting `mode="inline"` will display the app directly in the notebook output cell.
-
-# %%
-app.run_server(mode="inline")
-
-# %% [markdown]
-# When running in JupyterLab, with the `jupyterlab-dash` extension, setting `mode="jupyterlab"` will open the app in a tab in JupyterLab.
-#
-# ```python
-# app.run_server(mode="jupyterlab")
-# ```
-
-# %%
diff --git a/002-Methods/004-Dashboards/001-Dash/002-Examples/001-Covid19dynstat/assets/metadata.csv b/002-Methods/004-Dashboards/001-Dash/002-Examples/001-Covid19dynstat/assets/metadata.csv
deleted file mode 100644
index c8c8d5e9c9815df8be33e3722d331103d5752c56..0000000000000000000000000000000000000000
--- a/002-Methods/004-Dashboards/001-Dash/002-Examples/001-Covid19dynstat/assets/metadata.csv
+++ /dev/null
@@ -1,413 +0,0 @@
-countyID,LKType,LKName
-5334,StädteRegion,Aachen
-7131,LK,Ahrweiler
-9771,LK,Aichach-Friedberg
-8425,LK,Alb-Donau-Kreis
-16077,LK,Altenburger Land
-7132,LK,Altenkirchen
-15081,LK,Altmarkkreis Salzwedel
-9171,LK,Altötting
-7331,LK,Alzey-Worms
-9361,SK,Amberg
-9371,LK,Amberg-Sulzbach
-3451,LK,Ammerland
-15082,LK,Anhalt-Bitterfeld
-9561,SK,Ansbach
-9571,LK,Ansbach
-9661,SK,Aschaffenburg
-9671,LK,Aschaffenburg
-9772,LK,Augsburg
-9761,SK,Augsburg
-3452,LK,Aurich
-7332,LK,Bad Dürkheim
-9672,LK,Bad Kissingen
-7133,LK,Bad Kreuznach
-9173,LK,Bad Tölz-Wolfratshausen
-8211,SK,Baden-Baden
-9471,LK,Bamberg
-9461,SK,Bamberg
-12060,LK,Barnim
-14625,LK,Bautzen
-9472,LK,Bayreuth
-9462,SK,Bayreuth
-9172,LK,Berchtesgadener Land
-6431,LK,Bergstraße
-11004,SK,Berlin Charlottenburg-Wilmersdorf
-11002,SK,Berlin Friedrichshain-Kreuzberg
-11011,SK,Berlin Lichtenberg
-11010,SK,Berlin Marzahn-Hellersdorf
-11001,SK,Berlin Mitte
-11008,SK,Berlin Neukölln
-11003,SK,Berlin Pankow
-11012,SK,Berlin Reinickendorf
-11005,SK,Berlin Spandau
-11006,SK,Berlin Steglitz-Zehlendorf
-11007,SK,Berlin Tempelhof-Schöneberg
-11009,SK,Berlin Treptow-Köpenick
-7231,LK,Bernkastel-Wittlich
-8426,LK,Biberach
-5711,SK,Bielefeld
-7134,LK,Birkenfeld
-7232,LK,Bitburg-Prüm
-8115,LK,Böblingen
-5911,SK,Bochum
-8435,LK,Bodenseekreis
-5314,SK,Bonn
-15083,LK,Börde
-5554,LK,Borken
-5512,SK,Bottrop
-12051,SK,Brandenburg a.d.Havel
-3101,SK,Braunschweig
-8315,LK,Breisgau-Hochschwarzwald
-4011,SK,Bremen
-4012,SK,Bremerhaven
-15084,LK,Burgenlandkreis
-8235,LK,Calw
-3351,LK,Celle
-9372,LK,Cham
-14511,SK,Chemnitz
-3453,LK,Cloppenburg
-9463,SK,Coburg
-9473,LK,Coburg
-7135,LK,Cochem-Zell
-5558,LK,Coesfeld
-12052,SK,Cottbus
-3352,LK,Cuxhaven
-9174,LK,Dachau
-12061,LK,Dahme-Spreewald
-6411,SK,Darmstadt
-6432,LK,Darmstadt-Dieburg
-9271,LK,Deggendorf
-3401,SK,Delmenhorst
-15001,SK,Dessau-Roßlau
-3251,LK,Diepholz
-9773,LK,Dillingen a.d.Donau
-9279,LK,Dingolfing-Landau
-1051,LK,Dithmarschen
-9779,LK,Donau-Ries
-7333,LK,Donnersbergkreis
-5913,SK,Dortmund
-14612,SK,Dresden
-5112,SK,Duisburg
-5358,LK,Düren
-5111,SK,Düsseldorf
-9175,LK,Ebersberg
-16061,LK,Eichsfeld
-9176,LK,Eichstätt
-16056,SK,Eisenach
-12062,LK,Elbe-Elster
-3402,SK,Emden
-8316,LK,Emmendingen
-3454,LK,Emsland
-5954,LK,Ennepe-Ruhr-Kreis
-8236,LK,Enzkreis
-9177,LK,Erding
-16051,SK,Erfurt
-9562,SK,Erlangen
-9572,LK,Erlangen-Höchstadt
-14521,LK,Erzgebirgskreis
-5113,SK,Essen
-8116,LK,Esslingen
-5366,LK,Euskirchen
-1001,SK,Flensburg
-9474,LK,Forchheim
-7311,SK,Frankenthal
-12053,SK,Frankfurt (Oder)
-6412,SK,Frankfurt am Main
-8311,SK,Freiburg i.Breisgau
-9178,LK,Freising
-8237,LK,Freudenstadt
-9272,LK,Freyung-Grafenau
-3455,LK,Friesland
-6631,LK,Fulda
-9179,LK,Fürstenfeldbruck
-9563,SK,Fürth
-9573,LK,Fürth
-9180,LK,Garmisch-Partenkirchen
-5513,SK,Gelsenkirchen
-16052,SK,Gera
-7334,LK,Germersheim
-6531,LK,Gießen
-3151,LK,Gifhorn
-8117,LK,Göppingen
-14626,LK,Görlitz
-3153,LK,Goslar
-16067,LK,Gotha
-3159,LK,Göttingen
-3456,LK,Grafschaft Bentheim
-16076,LK,Greiz
-6433,LK,Groß-Gerau
-9774,LK,Günzburg
-5754,LK,Gütersloh
-5914,SK,Hagen
-15002,SK,Halle
-2000,SK,Hamburg
-3252,LK,Hameln-Pyrmont
-5915,SK,Hamm
-3241,Region,Hannover
-3353,LK,Harburg
-15085,LK,Harz
-9674,LK,Haßberge
-12063,LK,Havelland
-3358,LK,Heidekreis
-8221,SK,Heidelberg
-8135,LK,Heidenheim
-8121,SK,Heilbronn
-8125,LK,Heilbronn
-5370,LK,Heinsberg
-3154,LK,Helmstedt
-5758,LK,Herford
-5916,SK,Herne
-6632,LK,Hersfeld-Rotenburg
-1053,LK,Herzogtum Lauenburg
-16069,LK,Hildburghausen
-3254,LK,Hildesheim
-5958,LK,Hochsauerlandkreis
-6434,LK,Hochtaunuskreis
-9464,SK,Hof
-9475,LK,Hof
-8126,LK,Hohenlohekreis
-3255,LK,Holzminden
-5762,LK,Höxter
-16070,LK,Ilm-Kreis
-9161,SK,Ingolstadt
-16053,SK,Jena
-15086,LK,Jerichower Land
-7335,LK,Kaiserslautern
-7312,SK,Kaiserslautern
-8215,LK,Karlsruhe
-8212,SK,Karlsruhe
-6633,LK,Kassel
-6611,SK,Kassel
-9762,SK,Kaufbeuren
-9273,LK,Kelheim
-9763,SK,Kempten
-1002,SK,Kiel
-9675,LK,Kitzingen
-5154,LK,Kleve
-7111,SK,Koblenz
-5315,SK,Köln
-8335,LK,Konstanz
-5114,SK,Krefeld
-9476,LK,Kronach
-9477,LK,Kulmbach
-7336,LK,Kusel
-16065,LK,Kyffhäuserkreis
-6532,LK,Lahn-Dill-Kreis
-7313,SK,Landau i.d.Pfalz
-9181,LK,Landsberg a.Lech
-9261,SK,Landshut
-9274,LK,Landshut
-3457,LK,Leer
-14729,LK,Leipzig
-14713,SK,Leipzig
-5316,SK,Leverkusen
-9478,LK,Lichtenfels
-6533,LK,Limburg-Weilburg
-9776,LK,Lindau
-5766,LK,Lippe
-8336,LK,Lörrach
-1003,SK,Lübeck
-3354,LK,Lüchow-Dannenberg
-8118,LK,Ludwigsburg
-7314,SK,Ludwigshafen
-13076,LK,Ludwigslust/Parchim
-3355,LK,Lüneburg
-15003,SK,Magdeburg
-6435,LK,Main-Kinzig-Kreis
-9677,LK,Main-Spessart
-8128,LK,Main-Tauber-Kreis
-6436,LK,Main-Taunus-Kreis
-7315,SK,Mainz
-7339,LK,Mainz-Bingen
-8222,SK,Mannheim
-15087,LK,Mansfeld-Südharz
-6534,LK,Marburg-Biedenkopf
-12064,LK,Märkisch-Oderland
-5962,LK,Märkischer Kreis
-7137,LK,Mayen-Koblenz
-13071,LK,Mecklenburgische Seenplatte
-14627,LK,Meißen
-9764,SK,Memmingen
-10042,LK,Merzig-Wadern
-5158,LK,Mettmann
-9182,LK,Miesbach
-9676,LK,Miltenberg
-5770,LK,Minden-Lübbecke
-14522,LK,Mittelsachsen
-5116,SK,Mönchengladbach
-9183,LK,Mühldorf a.Inn
-5117,SK,Mülheim a.d.Ruhr
-9162,SK,München
-9184,LK,München
-5515,SK,Münster
-8225,LK,Neckar-Odenwald-Kreis
-9775,LK,Neu-Ulm
-9185,LK,Neuburg-Schrobenhausen
-9373,LK,Neumarkt i.d.OPf.
-1004,SK,Neumünster
-10043,LK,Neunkirchen
-9374,LK,Neustadt a.d.Waldnaab
-7316,SK,Neustadt a.d.Weinstraße
-9575,LK,Neustadt/Aisch-Bad Windsheim
-7138,LK,Neuwied
-3256,LK,Nienburg (Weser)
-1054,LK,Nordfriesland
-16062,LK,Nordhausen
-14730,LK,Nordsachsen
-13074,LK,Nordwestmecklenburg
-3155,LK,Northeim
-9564,SK,Nürnberg
-9574,LK,Nürnberger Land
-9780,LK,Oberallgäu
-5374,LK,Oberbergischer Kreis
-5119,SK,Oberhausen
-12065,LK,Oberhavel
-12066,LK,Oberspreewald-Lausitz
-6437,LK,Odenwaldkreis
-12067,LK,Oder-Spree
-6438,LK,Offenbach
-6413,SK,Offenbach
-3458,LK,Oldenburg
-3403,SK,Oldenburg
-5966,LK,Olpe
-8317,LK,Ortenaukreis
-3459,LK,Osnabrück
-3404,SK,Osnabrück
-8136,LK,Ostalbkreis
-9777,LK,Ostallgäu
-3356,LK,Osterholz
-1055,LK,Ostholstein
-12068,LK,Ostprignitz-Ruppin
-5774,LK,Paderborn
-9262,SK,Passau
-9275,LK,Passau
-3157,LK,Peine
-9186,LK,Pfaffenhofen a.d.Ilm
-8231,SK,Pforzheim
-1056,LK,Pinneberg
-7317,SK,Pirmasens
-1057,LK,Plön
-12054,SK,Potsdam
-12069,LK,Potsdam-Mittelmark
-12070,LK,Prignitz
-8216,LK,Rastatt
-8436,LK,Ravensburg
-5562,LK,Recklinghausen
-9276,LK,Regen
-9375,LK,Regensburg
-9362,SK,Regensburg
-8119,LK,Rems-Murr-Kreis
-5120,SK,Remscheid
-1058,LK,Rendsburg-Eckernförde
-8415,LK,Reutlingen
-5362,LK,Rhein-Erft-Kreis
-7140,LK,Rhein-Hunsrück-Kreis
-5162,LK,Rhein-Kreis Neuss
-7141,LK,Rhein-Lahn-Kreis
-8226,LK,Rhein-Neckar-Kreis
-7338,LK,Rhein-Pfalz-Kreis
-5382,LK,Rhein-Sieg-Kreis
-6439,LK,Rheingau-Taunus-Kreis
-5378,LK,Rheinisch-Bergischer Kreis
-9673,LK,Rhön-Grabfeld
-9163,SK,Rosenheim
-9187,LK,Rosenheim
-13072,LK,Rostock
-13003,SK,Rostock
-3357,LK,Rotenburg (Wümme)
-9576,LK,Roth
-9277,LK,Rottal-Inn
-8325,LK,Rottweil
-16074,LK,Saale-Holzland-Kreis
-16075,LK,Saale-Orla-Kreis
-15088,LK,Saalekreis
-16073,LK,Saalfeld-Rudolstadt
-10045,LK,Saar-Pfalz-Kreis
-10044,LK,Saarlouis
-14628,LK,Sächsische Schweiz-Osterzgebirge
-3102,SK,Salzgitter
-15089,LK,Salzlandkreis
-10046,LK,Sankt Wendel
-3257,LK,Schaumburg
-1059,LK,Schleswig-Flensburg
-16066,LK,Schmalkalden-Meiningen
-9565,SK,Schwabach
-8127,LK,Schwäbisch Hall
-6634,LK,Schwalm-Eder-Kreis
-9376,LK,Schwandorf
-8326,LK,Schwarzwald-Baar-Kreis
-9678,LK,Schweinfurt
-9662,SK,Schweinfurt
-13004,SK,Schwerin
-1060,LK,Segeberg
-5970,LK,Siegen-Wittgenstein
-8437,LK,Sigmaringen
-5974,LK,Soest
-5122,SK,Solingen
-16068,LK,Sömmerda
-16072,LK,Sonneberg
-7318,SK,Speyer
-12071,LK,Spree-Neiße
-3359,LK,Stade
-10041,LK,Stadtverband Saarbrücken
-9188,LK,Starnberg
-1061,LK,Steinburg
-5566,LK,Steinfurt
-15090,LK,Stendal
-1062,LK,Stormarn
-9263,SK,Straubing
-9278,LK,Straubing-Bogen
-8111,SK,Stuttgart
-7337,LK,Südliche Weinstraße
-7340,LK,Südwestpfalz
-16054,SK,Suhl
-12072,LK,Teltow-Fläming
-9377,LK,Tirschenreuth
-9189,LK,Traunstein
-7211,SK,Trier
-7235,LK,Trier-Saarburg
-8416,LK,Tübingen
-8327,LK,Tuttlingen
-12073,LK,Uckermark
-3360,LK,Uelzen
-8421,SK,Ulm
-5978,LK,Unna
-16064,LK,Unstrut-Hainich-Kreis
-9778,LK,Unterallgäu
-3460,LK,Vechta
-3361,LK,Verden
-5166,LK,Viersen
-6535,LK,Vogelsbergkreis
-14523,LK,Vogtlandkreis
-13075,LK,Vorpommern/Greifswald
-13073,LK,Vorpommern/Rügen
-7233,LK,Vulkaneifel
-6635,LK,Waldeck-Frankenberg
-8337,LK,Waldshut
-5570,LK,Warendorf
-16063,LK,Wartburgkreis
-9363,SK,Weiden i.d.OPf.
-9190,LK,Weilheim-Schongau
-16055,SK,Weimar
-16071,LK,Weimarer Land
-9577,LK,Weißenburg-Gunzenhausen
-6636,LK,Werra-Meißner-Kreis
-5170,LK,Wesel
-3461,LK,Wesermarsch
-7143,LK,Westerwaldkreis
-6440,LK,Wetteraukreis
-6414,SK,Wiesbaden
-3405,SK,Wilhelmshaven
-15091,LK,Wittenberg
-3462,LK,Wittmund
-3158,LK,Wolfenbüttel
-3103,SK,Wolfsburg
-7319,SK,Worms
-9479,LK,Wunsiedel i.Fichtelgebirge
-5124,SK,Wuppertal
-9663,SK,Würzburg
-9679,LK,Würzburg
-8417,LK,Zollernalbkreis
-7320,SK,Zweibrücken
-14524,LK,Zwickau
diff --git a/002-Methods/004-Dashboards/001-Dash/002-Examples/001-Covid19dynstat/covid19dynstat-dash.ipynb b/002-Methods/004-Dashboards/001-Dash/002-Examples/001-Covid19dynstat/covid19dynstat-dash.ipynb
deleted file mode 100644
index 3a005418bc2d4ff29539f4a43fe948735e0e6bcf..0000000000000000000000000000000000000000
--- a/002-Methods/004-Dashboards/001-Dash/002-Examples/001-Covid19dynstat/covid19dynstat-dash.ipynb
+++ /dev/null
@@ -1,1473 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Covid19dynstat - JupyterDash"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The `jupyter-dash` package makes it easy to develop Plotly Dash apps from the Jupyter Notebook and JupyterLab.\n",
- "Just replace the standard `dash.Dash` class with the `jupyter_dash.JupyterDash` subclass."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "from jupyter_dash import JupyterDash"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import dash\n",
- "import dash_core_components as dcc\n",
- "import dash_html_components as html\n",
- "import dash_bootstrap_components as dbc\n",
- "import dash_player\n",
- "import pandas as pd"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "When running in JupyterHub (or Binder), call the `infer_jupyter_config` function to detect the proxy configuration. This will detect the proper request_pathname_prefix and server_url values to use when displaying Dash apps. For example: \n",
- "server_url = `https://jupyter-jsc.fz-juelich.de` \n",
- "request_pathname_prefix = `/user/j.goebbert@fz-juelich.de/jureca_login/` \n",
- "For details please check the source here https://github.com/plotly/jupyter-dash/blob/v0.2.1.post1/jupyter_dash/comms.py#L33"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "JupyterDash.infer_jupyter_proxy_config()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Attention:** I have to run this cell twice: first press play, wait a bit and hit play again while it still shows `[*]`"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Create a Dash Flask server\n",
- "Requests the browser to load Bootstrap "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "# select a theme\n",
- "app = JupyterDash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])\n",
- "# external_stylesheets=[dbc.themes.BOOTSTRAP] -> default theme\n",
- "# external_stylesheets=[dbc.themes.CYBORG] -> dark theme\n",
- "\n",
- "app.title = 'Covid-19-Interaktionsmodell'\n",
- "\n",
- "# start the server\n",
- "server = app.server\n",
- "#print(app.get_asset_url('aaa'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "#import base64\n",
- "from textwrap import dedent\n",
- "from datetime import datetime as dt, timedelta\n",
- "from dash.dependencies import Input, Output, State\n",
- "\n",
- "asset_url=app.get_asset_url('assets') # = $JUPYTERHUB_SERVICE_PREFIX/assets\n",
- "# print(asset_url)\n",
- "# example: \"https://jupyter-jsc.fz-juelich.de/user/<user>@fz-juelich.de/<machine>/proxy/8050/assets/\"\n",
- "\n",
- "metadata = pd.read_csv(\"assets/metadata.csv\")\n",
- "\n",
- "min_date=dt(2020, 1, 29).date()\n",
- "max_date=dt(2020, 6, 16).date() # dt.today().date()\n",
- "init_date=dt(2020, 6, 16).date() # dt.today().date()\n",
- "init_countyid=1001\n",
- "deltadays = 26\n",
- "\n",
- "def get_assets_dir(date):\n",
- " date = dt.strptime(date.split(' ')[0], '%Y-%m-%d')\n",
- " assets_dir = (date -timedelta(days=deltadays)).strftime('%Y_%m_%d')\n",
- " return assets_dir"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Define the top navigation bar"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "#####################\n",
- "# Header and Footer\n",
- "#####################\n",
- "# https://dash-bootstrap-components.opensource.faculty.ai/docs/components/navbar/\n",
- "\n",
- "navbar = dbc.NavbarSimple(\n",
- " brand=\"Bayessches räumlich-zeitliches Interaktionsmodell für Covid-19\",\n",
- " brand_href=\"#\",\n",
- " color=\"dark\",\n",
- " fixed=\"top\",\n",
- " dark=True,\n",
- " children=[\n",
- " dbc.NavItem(\n",
- " dbc.NavLink(\n",
- " \"Artikel\",\n",
- " href=\"https://nbviewer.jupyter.org/github/neuroinfo-os/BSTIM-Covid19/blob/master/notebooks/visualization_final.ipynb\",\n",
- " )\n",
- " ),\n",
- " dbc.NavItem(\n",
- " dbc.NavLink(\n",
- " \"Quellcode\",\n",
- " href=\"https://github.com/neuroinfo-os/BSTIM-Covid19\",\n",
- " )\n",
- " ),\n",
- " ])\n",
- "\n",
- "navbar_footer = dbc.NavbarSimple(\n",
- " #brand=\"\",\n",
- " brand_href=\"#\",\n",
- " color=\"light\",\n",
- " #fixed=\"bottom\",\n",
- " #sticky=True,\n",
- " #dark=True, \n",
- " children=[\n",
- " dbc.NavItem(\n",
- " dbc.NavLink(\n",
- " \"Impressum\",\n",
- " href=\"https://www.fz-juelich.de/portal/DE/Service/Impressum/impressum_node.html\",\n",
- " )\n",
- " ),\n",
- " dbc.NavItem(\n",
- " dbc.NavLink(\n",
- " \"Datenschutz\",\n",
- " href=\"https://www.fz-juelich.de/portal/DE/datenschutz/_node.html\",\n",
- " )\n",
- " ),\n",
- " ])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "#####################\n",
- "# Disclaimer\n",
- "#####################\n",
- "disclaimer_modal = html.Div(\n",
- " [\n",
- " dbc.Button(\"Disclaimer\", id=\"disclaimer_modal_open\", outline=True, color=\"secondary\", className=\"mr-1\"),\n",
- " dbc.Modal(\n",
- " id=\"disclaimer_modal\",\n",
- " size=\"xl\",\n",
- " children=[\n",
- " dbc.ModalHeader(\"Disclaimer\"),\n",
- " dbc.ModalBody(\n",
- " children=[\n",
- " dcc.Markdown(\n",
- " f\"\"\"\n",
- " Für die Gesamtzahl der Infektionen pro Bundesland/Landkreis werden die den Gesundheitsämtern nach Infektionsschutzgesetz gemeldeten Fälle verwendet,\n",
- " die dem RKI bis zum jeweiligen Tag um 0 Uhr übermittelt wurden.\n",
- " Für die Analyse wird das Meldedatum verwendet, s. [Details zu den Daten](https://experience.arcgis.com/experience/478220a4c454480e823b17327b2bf1d4)\n",
- " Da es in dem Verfahren zu Differenzen zwischen Erkrankungsdatum und Meldedatum, sowie Verzögerungen in dem Meldeprozess geben kann,\n",
- " ist die Analyse der Fallzahlen der letzten Woche bereits eine Vorhersage, die auf einer Schätzung basiert.\n",
- " Alle hier präsentierten Ergebnisse basieren auf statistischen Methoden und bilden damit nicht das reale Geschehen, sondern Schätzungen ab, die von der wirklichen Situation abweichen können.\n",
- " Dies ist bei der Interpretation der Ergebnisse zu berücksichtigen. \n",
- " Für eine detailliertere Analyse der COVID-19-Fälle verweisen wir auf den [täglichen Lagebericht des RKI](https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Situationsberichte/Gesamt.html).\n",
- " \"\"\"\n",
- " ),\n",
- " ]\n",
- " ),\n",
- " dbc.ModalFooter(\n",
- " dbc.Button(\"Schließen\", id=\"disclaimer_modal_close\", className=\"ml-auto\")\n",
- " ),\n",
- " ],\n",
- " ),\n",
- " ]\n",
- ")\n",
- "@app.callback(\n",
- " Output(\"disclaimer_modal\", \"is_open\"),\n",
- " [Input(\"disclaimer_modal_open\", \"n_clicks\"), Input(\"disclaimer_modal_close\", \"n_clicks\")],\n",
- " [State(\"disclaimer_modal\", \"is_open\")],\n",
- ")\n",
- "def toggle_modal(n1, n2, is_open):\n",
- " if n1 or n2:\n",
- " return not is_open\n",
- " return is_open"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "#####################\n",
- "# Date-Tabs (left)\n",
- "#####################\n",
- "left_date_tab1_modal = html.Div(\n",
- " [\n",
- " dbc.Button(\"Vergrößern\", id=\"left_date_tab1_modal_open\", outline=True, color=\"secondary\", className=\"mr-1\"),\n",
- " dbc.Modal(\n",
- " id=\"left_date_tab1_modal\",\n",
- " size=\"xl\",\n",
- " children=[\n",
- " dbc.ModalHeader(\"Infektionen\"),\n",
- " dbc.ModalBody(\n",
- " children=[\n",
- " html.Img(\n",
- " id=\"left_date_modal1_img\",\n",
- " src=asset_url + init_date.strftime('%Y_%m_%d') + \"/map.png\",\n",
- " style={'width':'100%', 'height':'100%'},\n",
- " ),\n",
- " ]\n",
- " ),\n",
- " dbc.ModalFooter(\n",
- " dbc.Button(\"Schließen\", id=\"left_date_tab1_modal_close\", className=\"ml-auto\")\n",
- " ),\n",
- " ],\n",
- " ),\n",
- " ]\n",
- ")\n",
- "left_date_tab1 = dbc.Card(\n",
- " outline=True,\n",
- " color=\"light\",\n",
- " className=\"mt-3\",\n",
- " children=[ dbc.CardBody(\n",
- " children=[\n",
- " html.Div(\n",
- " id=\"left_date_tab1_img_div\",\n",
- " children=[\n",
- " left_date_tab1_modal,\n",
- " html.Img(\n",
- " id=\"left_date_tab1_img\",\n",
- " src=asset_url + init_date.strftime('%Y_%m_%d') + \"/map.png\",\n",
- " style={'width':'100%', 'height':'100%'},\n",
- " ),\n",
- " dbc.Tooltip(\n",
- " \"Die Infektionszahlen pro Tag (Welcher Tag (Nowcast ? Luke ) ) pro Landkreis und gewähltem Zeitfenster. \"\n",
- " \"Der angezeigte Wert entspricht dem Nowcast, also der Schätzung der Anzahl der tatsächlich Neuinfizierten. \"\n",
- " \"Diese Schätzung korrigiert die gemeldeten Zahlen, die aufgrund von Verzögerungen im Meldeprozess \"\n",
- " \"und einem unbekannten Erkrankungsdatum kleiner als die tatsächlichen Zahlen sein können, auf der Basis einer Vorhersage. \",\n",
- " target=\"left_date_tab1_img\",\n",
- " style={\"width\": \"200%\"},\n",
- " placement=\"left\",\n",
- " ),\n",
- " ]),\n",
- " ]),\n",
- " ])\n",
- "@app.callback(\n",
- " Output(\"left_date_tab1_modal\", \"is_open\"),\n",
- " [Input(\"left_date_tab1_img_div\", \"n_clicks\"), Input(\"left_date_tab1_modal_open\", \"n_clicks\"), Input(\"left_date_tab1_modal_close\", \"n_clicks\")],\n",
- " [State(\"left_date_tab1_modal\", \"is_open\")],\n",
- ")\n",
- "def toggle_modal(n1, n2, n3, is_open):\n",
- " if n1 or n2 or n3:\n",
- " return not is_open\n",
- " return is_open\n",
- "\n",
- "#####################\n",
- "\n",
- "left_date_tab2_modal = html.Div(\n",
- " [\n",
- " dbc.Button(\"Vergrößern\", id=\"left_date_tab2_modal_open\", outline=True, color=\"secondary\", className=\"mr-1\"),\n",
- " dbc.Modal(\n",
- " id=\"left_date_tab2_modal\",\n",
- " size=\"xl\",\n",
- " children=[\n",
- " dbc.ModalHeader(\"Interaktionskernel\"),\n",
- " dbc.ModalBody(\n",
- " children=[\n",
- " html.Img(\n",
- " id=\"left_date_modal2_img\",\n",
- " src=asset_url + init_date.strftime('%Y_%m_%d') + \"/interaction_kernel.png\",\n",
- " style={'width':'100%', 'height':'100%'},\n",
- " ),\n",
- " ]\n",
- " ),\n",
- " dbc.ModalFooter(\n",
- " dbc.Button(\"Schließen\", id=\"left_date_tab2_modal_close\", className=\"ml-auto\")\n",
- " ),\n",
- " ],\n",
- " ),\n",
- " ]\n",
- ") \n",
- "left_date_tab2 = dbc.Card(\n",
- " outline=True,\n",
- " color=\"light\",\n",
- " className=\"mt-3\", \n",
- " children=[ dbc.CardBody(\n",
- " children=[\n",
- " html.Div(\n",
- " id=\"left_date_tab2_img_div\",\n",
- " children=[\n",
- " left_date_tab2_modal,\n",
- " html.Img(\n",
- " id=\"left_date_tab2_img\",\n",
- " src=asset_url + init_date.strftime('%Y_%m_%d') + \"/interaction_kernel.png\",\n",
- " style={'width':'100%', 'height':'100%'},\n",
- " ),\n",
- " dbc.Tooltip(\n",
- " \"Der Interaktionskernel schätzt ab um wie stark eine gemeldete Infektion eine Neuansteckung in den nächsten Tagen \"\n",
- " \"in einem Umkreis von bis zu 50km beeinflusst. \"\n",
- " \"Diese Interaktion ist ein zusätzlicher Faktor der den Trend in einem Landkreis verstärkt oder abschwächt. \"\n",
- " \"Eine warme Farbe indiziert, dass eine Covid-19 Meldung eine erhöhte Wahrscheinlichkeit einer Neuinfektion \"\n",
- " \"im Verhältnis zum Trend zur Folge hat. \"\n",
- " \"Eine starke Farben in der Nähe kleiner Radien bedeutet, dass das Infektionsgeschehen vor allem Auswirkungen \"\n",
- " \"in der direkten Nähe der gemeldeten Fälle zur Folge hat. \"\n",
- " \"Die Interaktion basiert auf einer Schätzung der Bevölkerungsdichte und der Form der Landkreise. \"\n",
- " \"Daten zu den Wohnorten der Infizierten werden in dem Model nicht genutzt. \"\n",
- " \"Alle hier genutzten Daten sind vollständig anonymisiert (siehe Erklärvideo). \"\n",
- " \"Bei der Interpretation der Interaktionskernel ist dies zu berücksichtigen, und wir weisen darauf hin, dass dies nur eine Schätzung ist \"\n",
- " \"die von der Realität abweichen kann.\",\n",
- " target=\"left_date_tab2_img\",\n",
- " style={\"width\": \"200%\"},\n",
- " placement=\"left\",\n",
- " ),\n",
- " ]),\n",
- " ]),\n",
- " ])\n",
- "@app.callback(\n",
- " Output(\"left_date_tab2_modal\", \"is_open\"),\n",
- " [Input(\"left_date_tab2_img_div\", \"n_clicks\"), Input(\"left_date_tab2_modal_open\", \"n_clicks\"), Input(\"left_date_tab2_modal_close\", \"n_clicks\")],\n",
- " [State(\"left_date_tab2_modal\", \"is_open\")],\n",
- ")\n",
- "def toggle_modal(n1, n2, n3, is_open):\n",
- " if n1 or n2 or n3:\n",
- " return not is_open\n",
- " return is_open"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "#####################\n",
- "# Date-Window Picker (left)\n",
- "#####################\n",
- "left_date_controls = dbc.FormGroup(\n",
- " children=[\n",
- " dbc.Label(\n",
- " id='left_date-label',\n",
- " children=[\"Datumsauswahl:\"],\n",
- " ),\n",
- " html.Div(\n",
- " children=[\n",
- " dcc.DatePickerSingle(\n",
- " id='left_date-picker',\n",
- " display_format='DD. MMM YYYY',\n",
- " min_date_allowed=min_date,\n",
- " max_date_allowed=max_date,\n",
- " initial_visible_month=init_date,\n",
- " date=init_date,\n",
- " ),\n",
- " html.Div(\n",
- " id='left_output-container-date-picker',\n",
- " #style={'display': 'none'},\n",
- " children=[(init_date -timedelta(days=deltadays)).strftime('%Y_%m_%d')],\n",
- " ),\n",
- " ]),\n",
- " ])\n",
- "\n",
- "# Date Picker\n",
- "@app.callback(\n",
- " Output(component_id='left_output-container-date-picker', component_property='children'),\n",
- " [Input(component_id='left_date-picker', component_property='date')])\n",
- "def update_left_date_picker(date):\n",
- " if date is not None:\n",
- " return get_assets_dir(date)\n",
- "\n",
- "# Map\n",
- "@app.callback(\n",
- " Output(component_id='left_date_tab1_img', component_property='src'),\n",
- " [Input(component_id='left_date-picker', component_property='date')])\n",
- "def update_left_date_tab1_img(date):\n",
- " if date is not None:\n",
- " assets_dir = get_assets_dir(date)\n",
- " return asset_url + assets_dir + \"/map.png\"\n",
- "@app.callback(\n",
- " Output(component_id='left_date_modal1_img', component_property='src'),\n",
- " [Input(component_id='left_date-picker', component_property='date')])\n",
- "def update_left_date_modal1_img(date):\n",
- " if date is not None:\n",
- " assets_dir = get_assets_dir(date)\n",
- " return asset_url + assets_dir + \"/map.png\"\n",
- "\n",
- "# Interaction Kernel\n",
- "@app.callback(\n",
- " Output(component_id='left_date_tab2_img', component_property='src'),\n",
- " [Input(component_id='left_date-picker', component_property='date')])\n",
- "def update_left_date_tab2_img(date):\n",
- " if date is not None:\n",
- " assets_dir = get_assets_dir(date)\n",
- " return asset_url + assets_dir + \"/interaction_kernel.png\"\n",
- "@app.callback(\n",
- " Output(component_id='left_date_modal2_img', component_property='src'),\n",
- " [Input(component_id='left_date-picker', component_property='date')])\n",
- "def update_left_date_modal2_img(date):\n",
- " if date is not None:\n",
- " assets_dir = get_assets_dir(date)\n",
- " return asset_url + assets_dir + \"/interaction_kernel.png\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "#####################\n",
- "# Date-Tabs (right)\n",
- "#####################\n",
- "right_date_tab1_modal = html.Div(\n",
- " [\n",
- " dbc.Button(\"Vergrößern\", id=\"right_date_tab1_modal_open\", outline=True, color=\"secondary\", className=\"mr-1\"),\n",
- " dbc.Modal(\n",
- " id=\"right_date_tab1_modal\",\n",
- " size=\"xl\",\n",
- " children=[\n",
- " dbc.ModalHeader(\"Infektionen\"),\n",
- " dbc.ModalBody(\n",
- " children=[\n",
- " html.Img(\n",
- " id=\"right_date_modal1_img\",\n",
- " src=asset_url + init_date.strftime('%Y_%m_%d') + \"/map.png\",\n",
- " style={'width':'100%', 'height':'100%'},\n",
- " ),\n",
- " ]\n",
- " ),\n",
- " dbc.ModalFooter(\n",
- " dbc.Button(\"Schließen\", id=\"right_date_tab1_modal_close\", className=\"ml-auto\")\n",
- " ),\n",
- " ],\n",
- " ),\n",
- " ]\n",
- ")\n",
- "right_date_tab1 = dbc.Card(\n",
- " outline=True,\n",
- " color=\"light\", \n",
- " className=\"mt-3\",\n",
- " children=[ dbc.CardBody(\n",
- " children=[\n",
- " html.Div(\n",
- " id=\"right_date_tab1_img_div\",\n",
- " children=[\n",
- " right_date_tab1_modal,\n",
- " html.Img(\n",
- " id=\"right_date_tab1_img\",\n",
- " src=asset_url + init_date.strftime('%Y_%m_%d') + \"/map.png\",\n",
- " style={'width':'100%', 'height':'100%'},\n",
- " ),\n",
- " dbc.Tooltip(\n",
- " \"Die Infektionszahlen pro Tag (Welcher Tag (Nowcast ? Luke ) ) pro Landkreis und gewählten Zeitfenster. \"\n",
- " \"Der anzeigte Wert entspricht dem Nowcast, also der Schätzung der Anzahl der tatsächlich neuinfizierten. \"\n",
- " \"Diese Schätzung korrigiert die gemeldeten Zahlen, die aufgrund von Verzögerungen im Meldeprozess \"\n",
- " \"und einem unbekannten Erkrankungsdatum kleiner als die tatsächlichen Zahlen sein können, auf der Basis einer Vorhersage. \",\n",
- " target=\"right_date_tab1_img\",\n",
- " style={\"width\": \"200%\"},\n",
- " placement=\"right\",\n",
- " ),\n",
- " ]),\n",
- " ]),\n",
- " ])\n",
- "@app.callback(\n",
- " Output(\"right_date_tab1_modal\", \"is_open\"),\n",
- " [Input(\"right_date_tab1_img_div\", \"n_clicks\"), Input(\"right_date_tab1_modal_open\", \"n_clicks\"), Input(\"right_date_tab1_modal_close\", \"n_clicks\")],\n",
- " [State(\"right_date_tab1_modal\", \"is_open\")],\n",
- ")\n",
- "def toggle_modal(n1, n2, n3, is_open):\n",
- " if n1 or n2 or n3:\n",
- " return not is_open\n",
- " return is_open\n",
- "\n",
- "#####################\n",
- "\n",
- "right_date_tab2_modal = html.Div(\n",
- " [\n",
- " dbc.Button(\"Vergrößern\", id=\"right_date_tab2_modal_open\", outline=True, color=\"secondary\", className=\"mr-1\"),\n",
- " dbc.Modal(\n",
- " id=\"right_date_tab2_modal\",\n",
- " size=\"xl\",\n",
- " children=[\n",
- " dbc.ModalHeader(\"Interaktionskernel\"),\n",
- " dbc.ModalBody(\n",
- " children=[\n",
- " html.Img(\n",
- " id=\"right_date_modal2_img\",\n",
- " src=asset_url + init_date.strftime('%Y_%m_%d') + \"/interaction_kernel.png\",\n",
- " style={'width':'100%', 'height':'100%'},\n",
- " ),\n",
- " ]\n",
- " ),\n",
- " dbc.ModalFooter(\n",
- " dbc.Button(\"Schließen\", id=\"right_date_tab2_modal_close\", className=\"ml-auto\")\n",
- " ),\n",
- " ],\n",
- " ),\n",
- " ]\n",
- ") \n",
- "right_date_tab2 = dbc.Card(\n",
- " outline=True,\n",
- " color=\"light\", \n",
- " className=\"mt-3\", \n",
- " children=[ dbc.CardBody(\n",
- " children=[\n",
- " html.Div(\n",
- " id=\"right_date_tab2_img_div\",\n",
- " children=[\n",
- " right_date_tab2_modal,\n",
- " html.Img(\n",
- " id=\"right_date_tab2_img\",\n",
- " src=asset_url + init_date.strftime('%Y_%m_%d') + \"/interaction_kernel.png\",\n",
- " style={'width':'100%', 'height':'100%'},\n",
- " ),\n",
- " dbc.Tooltip(\n",
- " \"Der Interaktionskernel schätzt ab um wie stark eine gemeldete Infektion eine Neuansteckung in den nächsten Tagen \"\n",
- " \"in einem Umkreis von bis zu 50km beeinflusst. \"\n",
- " \"Diese Interaktion ist ein zusätzlicher Faktor der den Trend in einem Landkreis verstärkt oder abschwächt. \"\n",
- " \"Eine warme Farbe indiziert, dass eine Covid-19 Meldung eine erhöhte Wahrscheinlichkeit einer Neuinfektion \"\n",
- " \"im Verhältnis zum Trend zur Folge hat. \"\n",
- " \"Eine starke Farben in der Nähe kleiner Radien bedeutet, dass das Infektionsgeschehen vor allem Auswirkungen \"\n",
- " \"in der direkten Nähe der gemeldeten Fälle zur Folge hat. \"\n",
- " \"Die Interaktion basiert auf einer Schätzung der Bevölkerungsdichte und der Form der Landkreise. \"\n",
- " \"Daten zu den Wohnorten der Infizierten werden in dem Model nicht genutzt. \"\n",
- " \"Alle hier genutzten Daten sind vollständig anonymisiert (siehe Erklärvideo). \"\n",
- " \"Bei der Interpretation der Interaktionskernel ist dies zu berücksichtigen, und wir weisen darauf hin, dass dies nur eine Schätzung ist \"\n",
- " \"die von der Realität abweichen kann.\",\n",
- " target=\"right_date_tab2_img\",\n",
- " style={\"width\": \"200%\"},\n",
- " placement=\"right\",\n",
- " ),\n",
- " ]),\n",
- " ]),\n",
- " ])\n",
- "@app.callback(\n",
- " Output(\"right_date_tab2_modal\", \"is_open\"),\n",
- " [Input(\"right_date_tab2_img_div\", \"n_clicks\"), Input(\"right_date_tab2_modal_open\", \"n_clicks\"), Input(\"right_date_tab2_modal_close\", \"n_clicks\")],\n",
- " [State(\"right_date_tab2_modal\", \"is_open\")],\n",
- ")\n",
- "def toggle_modal(n1, n2, n3, is_open):\n",
- " if n1 or n2 or n3:\n",
- " return not is_open\n",
- " return is_open\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "#####################\n",
- "# Date-Window Picker (right)\n",
- "#####################\n",
- "right_date_controls = dbc.FormGroup(\n",
- " children=[\n",
- " dbc.Label(\n",
- " id='right_date-label',\n",
- " children=[\"Datumsauswahl:\"],\n",
- " ),\n",
- " html.Div(\n",
- " children=[\n",
- " dcc.DatePickerSingle(\n",
- " id='right_date-picker',\n",
- " display_format='DD. MMM YYYY',\n",
- " min_date_allowed=min_date,\n",
- " max_date_allowed=max_date,\n",
- " initial_visible_month=init_date,\n",
- " date=init_date,\n",
- " ),\n",
- " html.Div(\n",
- " id='right_output-container-date-picker',\n",
- " #style={'display': 'none'},\n",
- " children=[init_date.strftime('%Y_%m_%d')],\n",
- " ),\n",
- " ]),\n",
- " ])\n",
- "\n",
- "# Date Picker\n",
- "@app.callback(\n",
- " Output(component_id='right_output-container-date-picker', component_property='children'),\n",
- " [Input(component_id='right_date-picker', component_property='date')])\n",
- "def update_right_date_picker(date):\n",
- " if date is not None:\n",
- " return get_assets_dir(date)\n",
- "\n",
- "# Map\n",
- "@app.callback(\n",
- " Output(component_id='right_date_tab1_img', component_property='src'),\n",
- " [Input(component_id='right_date-picker', component_property='date')])\n",
- "def update_right_date_tab1_img(date):\n",
- " if date is not None:\n",
- " assets_dir = get_assets_dir(date)\n",
- " return asset_url + assets_dir + \"/map.png\"\n",
- "@app.callback(\n",
- " Output(component_id='right_date_modal1_img', component_property='src'),\n",
- " [Input(component_id='right_date-picker', component_property='date')])\n",
- "def update_right_date_modal1_img(date):\n",
- " if date is not None:\n",
- " assets_dir = get_assets_dir(date)\n",
- " return asset_url + assets_dir + \"/map.png\"\n",
- "\n",
- "# Interaction Kernel\n",
- "@app.callback(\n",
- " Output(component_id='right_date_tab2_img', component_property='src'),\n",
- " [Input(component_id='right_date-picker', component_property='date')])\n",
- "def update_right_date_tab2_img(date):\n",
- " if date is not None:\n",
- " assets_dir = get_assets_dir(date)\n",
- " return asset_url + assets_dir + \"/interaction_kernel.png\"\n",
- "@app.callback(\n",
- " Output(component_id='right_date_modal2_img', component_property='src'),\n",
- " [Input(component_id='right_date-picker', component_property='date')])\n",
- "def update_right_date_modal2_img(date):\n",
- " if date is not None:\n",
- " assets_dir = get_assets_dir(date)\n",
- " return asset_url + assets_dir + \"/interaction_kernel.png\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [],
- "source": [
- "#####################\n",
- "# County-Tabs (left)\n",
- "#####################\n",
- "left_pos_tab1_modal = html.Div(\n",
- " [\n",
- " dbc.Button(\"Vergrößern\", id=\"left_pos_tab1_modal_open\", outline=True, color=\"secondary\", className=\"mr-1\"),\n",
- " dbc.Modal(\n",
- " id=\"left_pos_tab1_modal\",\n",
- " size=\"xl\",\n",
- " children=[\n",
- " dbc.ModalHeader(\"geglättet\"),\n",
- " dbc.ModalBody(\n",
- " children=[\n",
- " html.Img(\n",
- " id=\"left_pos_modal1_img\",\n",
- " src=asset_url + init_date.strftime('%Y_%m_%d') + \"/curve_{0:05d}.png\".format(init_countyid),\n",
- " style={'width':'100%', 'height':'100%'},\n",
- " ),\n",
- " ]\n",
- " ),\n",
- " dbc.ModalFooter(\n",
- " dbc.Button(\"Schließen\", id=\"left_pos_tab1_modal_close\", className=\"ml-auto\")\n",
- " ),\n",
- " ],\n",
- " ),\n",
- " ]\n",
- ") \n",
- "left_pos_tab1 = dbc.Card(\n",
- " outline=True,\n",
- " color=\"light\",\n",
- " className=\"mt-3\",\n",
- " children=[ dbc.CardBody(\n",
- " children=[\n",
- " html.Div(\n",
- " id=\"left_pos_tab1_img_div\",\n",
- " children=[\n",
- " left_pos_tab1_modal,\n",
- " html.Img(\n",
- " id=\"left_pos_tab1_img\",\n",
- " src=asset_url + init_date.strftime('%Y_%m_%d') + \"/curve_{0:05d}.png\".format(init_countyid),\n",
- " style={'width':'100%', 'height':'100%'},\n",
- " ),\n",
- " dbc.Tooltip(\n",
- " \"Analyse und Vorhersage der Infektionszahlen für den ausgewählten Landkreis. \"\n",
- " \"Der Nowcast entspricht der Schätzung der realen aktuellen Neuinfektionen für den angegebenden Tag. \"\n",
- " \"Diese Schätzung korrigiert die gemeldeten Zahlen, die aufgrund von Verzögerungen im Meldeprozess \"\n",
- " \"und einem unbekannten Erkrankungsdatum kleiner als die tatsächlichen Zahlen sein können, auf der Basis einer Vorhersage. \"\n",
- " \"Die Vorhersage nutzt das gleiche Modell um den Verlauf der kommenden 7 Tage, für die noch keine Zahlen vorliegen, vorherzusagen. \"\n",
- " \"Das geglättete Model korrigiert die Ergebnisse bezüglich eines Wochenrhythmusses bei den Meldeverzögerungen (siehe Erklärvideo). \",\n",
- " target=\"left_pos_tab1_img\",\n",
- " style={\"width\": \"600px\"},\n",
- " placement=\"left\",\n",
- " ),\n",
- " ]),\n",
- " ]),\n",
- " ])\n",
- "@app.callback(\n",
- " Output(\"left_pos_tab1_modal\", \"is_open\"),\n",
- " [Input(\"left_pos_tab1_img_div\", \"n_clicks\"), Input(\"left_pos_tab1_modal_open\", \"n_clicks\"), Input(\"left_pos_tab1_modal_close\", \"n_clicks\")],\n",
- " [State(\"left_pos_tab1_modal\", \"is_open\")],\n",
- ")\n",
- "def toggle_modal(n1, n2, n3, is_open):\n",
- " if n1 or n2 or n3:\n",
- " return not is_open\n",
- " return is_open\n",
- "\n",
- "#####################\n",
- "\n",
- "left_pos_tab2_modal = html.Div(\n",
- " [\n",
- " dbc.Button(\"Vergrößern\", id=\"left_pos_tab2_modal_open\", outline=True, color=\"secondary\", className=\"mr-1\"),\n",
- " dbc.Modal(\n",
- " id=\"left_pos_tab2_modal\",\n",
- " size=\"xl\",\n",
- " children=[\n",
- " dbc.ModalHeader(\"geglättet\"),\n",
- " dbc.ModalBody(\n",
- " children=[\n",
- " html.Img(\n",
- " id=\"left_pos_modal2_img\",\n",
- " src=asset_url + init_date.strftime('%Y_%m_%d') + \"/curve_trend_{0:05d}.png\".format(init_countyid),\n",
- " style={'width':'100%', 'height':'100%'},\n",
- " ),\n",
- " ]\n",
- " ),\n",
- " dbc.ModalFooter(\n",
- " dbc.Button(\"Schließen\", id=\"left_pos_tab2_modal_close\", className=\"ml-auto\")\n",
- " ),\n",
- " ],\n",
- " ),\n",
- " ]\n",
- ")\n",
- "left_pos_tab2 = dbc.Card(\n",
- " outline=True,\n",
- " color=\"light\",\n",
- " className=\"mt-3\",\n",
- " children=[ dbc.CardBody(\n",
- " children=[\n",
- " html.Div(\n",
- " id=\"left_pos_tab2_img_div\",\n",
- " children=[\n",
- " left_pos_tab2_modal,\n",
- " html.Img(\n",
- " id=\"left_pos_tab2_img\",\n",
- " src=asset_url + init_date.strftime('%Y_%m_%d') + \"/curve_trend_{0:05d}.png\".format(init_countyid),\n",
- " style={'width':'100%', 'height':'100%'},\n",
- " ),\n",
- " dbc.Tooltip(\n",
- " \"Analyse und Vorhersage der Infektionszahlen für den ausgewählten Landkreis. \"\n",
- " \"Der Nowcast entspricht der Schätzung der realen aktuellen Neuinfektionen für den angegebenden Tag. \"\n",
- " \"Diese Schätzung korrigiert die gemeldeten Zahlen, die aufgrund von Verzögerungen im Meldeprozess \"\n",
- " \"und einem unbekannten Erkrankungsdatum kleiner als die tatsächlichen Zahlen sein können, auf der Basis einer Vorhersage. \"\n",
- " \"Die Vorhersage nutzt das gleiche Modell um den Verlauf der kommenden 7 Tage, für die noch keine Zahlen vorliegen, vorherzusagen. \"\n",
- " \"Das geglättete Model korrigiert die Ergebnisse bezüglich eines Wochenrhythmusses bei den Meldeverzögerungen (siehe Erklärvideo). \",\n",
- " target=\"left_pos_tab2_img\",\n",
- " style={\"width\": \"200%\"},\n",
- " placement=\"left\",\n",
- " ),\n",
- " ]),\n",
- " ]),\n",
- " ])\n",
- "@app.callback(\n",
- " Output(\"left_pos_tab2_modal\", \"is_open\"),\n",
- " [Input(\"left_pos_tab2_img_div\", \"n_clicks\"), Input(\"left_pos_tab2_modal_open\", \"n_clicks\"), Input(\"left_pos_tab2_modal_close\", \"n_clicks\")],\n",
- " [State(\"left_pos_tab2_modal\", \"is_open\")],\n",
- ")\n",
- "def toggle_modal(n1, n2, n3, is_open):\n",
- " if n1 or n2 or n3:\n",
- " return not is_open\n",
- " return is_open"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [],
- "source": [
- "#####################\n",
- "# County Picker (left)\n",
- "#####################\n",
- "left_pos_controls = dbc.FormGroup(\n",
- " children=[\n",
- " dbc.Label(\n",
- " id='left_pos-label',\n",
- " children=[\"Wähle Landkreis:\"],\n",
- " ),\n",
- " html.Div(\n",
- " children=[\n",
- " dcc.Dropdown(\n",
- " id=\"left_pos-variable\",\n",
- " value=init_countyid,\n",
- " options=[\n",
- " {\"label\": row['LKName'] + \" (\" + row['LKType'] + \")\", \"value\": row['countyID']} for index, row in metadata.iterrows()\n",
- " ]),\n",
- " html.Div(id='left_output-container-pos-variable'), #, style={'display': 'none'}),\n",
- " ]), \n",
- " ])\n",
- "\n",
- "# County Picker\n",
- "@app.callback(\n",
- " Output(component_id='left_output-container-pos-variable', component_property='children'),\n",
- " [Input(component_id='left_pos-variable', component_property='value'),\n",
- " Input(component_id='left_output-container-date-picker', component_property='children')])\n",
- "def update_left_pos_variable(value, assets_dir):\n",
- " if value is not None:\n",
- " return asset_url + assets_dir + \"/\" + \"curve_trend_{0:05d}.png\".format(value)\n",
- "\n",
- "# geglättet\n",
- "@app.callback(\n",
- " Output(component_id='left_pos_tab1_img', component_property='src'),\n",
- " [Input(component_id='left_pos-variable', component_property='value'),\n",
- " Input(component_id='left_output-container-date-picker', component_property='children')])\n",
- "def update_left_pos_tab1_img(value, assets_dir):\n",
- " if value is not None:\n",
- " return asset_url + assets_dir + \"/\" + \"curve_trend_{0:05d}.png\".format(value)\n",
- "@app.callback(\n",
- " Output(component_id='left_pos_modal1_img', component_property='src'),\n",
- " [Input(component_id='left_pos-variable', component_property='value'),\n",
- " Input(component_id='left_output-container-date-picker', component_property='children')])\n",
- "def update_left_pos_modal1_img(value, assets_dir):\n",
- " if value is not None:\n",
- " return asset_url + assets_dir + \"/\" + \"curve_trend_{0:05d}.png\".format(value)\n",
- "\n",
- "# ungeglättet\n",
- "@app.callback(\n",
- " Output(component_id='left_pos_tab2_img', component_property='src'),\n",
- " [Input(component_id='left_pos-variable', component_property='value'),\n",
- " Input(component_id='left_output-container-date-picker', component_property='children')])\n",
- "def update_left_pos_tab2_img(value, assets_dir):\n",
- " if value is not None:\n",
- " return asset_url + assets_dir + \"/\" + \"curve_{0:05d}.png\".format(value)\n",
- "@app.callback(\n",
- " Output(component_id='left_pos_modal2_img', component_property='src'),\n",
- " [Input(component_id='left_pos-variable', component_property='value'),\n",
- " Input(component_id='left_output-container-date-picker', component_property='children')])\n",
- "def update_left_pos_modal2_img(value, assets_dir):\n",
- " if value is not None:\n",
- " return asset_url + assets_dir + \"/\" + \"curve_{0:05d}.png\".format(value)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [],
- "source": [
- "#####################\n",
- "# County-Tabs (right)\n",
- "#####################\n",
- "right_pos_tab1_modal = html.Div(\n",
- " [\n",
- " dbc.Button(\"Vergrößern\", id=\"right_pos_tab1_modal_open\", outline=True, color=\"secondary\", className=\"mr-1\"),\n",
- " dbc.Modal(\n",
- " id=\"right_pos_tab1_modal\",\n",
- " size=\"xl\",\n",
- " children=[\n",
- " dbc.ModalHeader(\"geglättet\"),\n",
- " dbc.ModalBody(\n",
- " children=[\n",
- " html.Img(\n",
- " id=\"right_pos_modal1_img\",\n",
- " src=asset_url + init_date.strftime('%Y_%m_%d') + \"/curve_{0:05d}.png\".format(init_countyid),\n",
- " style={'width':'100%', 'height':'100%'},\n",
- " ),\n",
- " ]\n",
- " ),\n",
- " dbc.ModalFooter(\n",
- " dbc.Button(\"Schließen\", id=\"right_pos_tab1_modal_close\", className=\"ml-auto\")\n",
- " ),\n",
- " ],\n",
- " ),\n",
- " ]\n",
- ") \n",
- "right_pos_tab1 = dbc.Card(\n",
- " outline=True,\n",
- " color=\"light\",\n",
- " className=\"mt-3\",\n",
- " children=[ dbc.CardBody(\n",
- " children=[\n",
- " html.Div(\n",
- " id=\"right_pos_tab1_img_div\",\n",
- " children=[\n",
- " right_pos_tab1_modal,\n",
- " html.Img(\n",
- " id=\"right_pos_tab1_img\",\n",
- " src=asset_url + init_date.strftime('%Y_%m_%d') + \"/curve_{0:05d}.png\".format(init_countyid),\n",
- " style={'width':'100%', 'height':'100%'},\n",
- " ),\n",
- " dbc.Tooltip(\n",
- " \"Analyse und Vorhersage der Infektionszahlen für den ausgewählten Landkreis. \"\n",
- " \"Der Nowcast entspricht der Schätzung der realen aktuellen Neuinfektionen für den angegebenden Tag. \"\n",
- " \"Diese Schätzung korrigiert die gemeldeten Zahlen, die aufgrund von Verzögerungen im Meldeprozess \"\n",
- " \"und einem unbekannten Erkrankungsdatum kleiner als die tatsächlichen Zahlen sein können, auf der Basis einer Vorhersage. \"\n",
- " \"Die Vorhersage nutzt das gleiche Modell um den Verlauf der kommenden 7 Tage, für die noch keine Zahlen vorliegen, vorherzusagen. \"\n",
- " \"Das geglättete Model korrigiert die Ergebnisse bezüglich eines Wochenrhythmusses bei den Meldeverzögerungen (siehe Erklärvideo). \",\n",
- " target=\"right_pos_tab1_img\",\n",
- " style={\"width\": \"200%\"},\n",
- " placement=\"right\",\n",
- " ),\n",
- " ]),\n",
- " ]),\n",
- " ])\n",
- "@app.callback(\n",
- " Output(\"right_pos_tab1_modal\", \"is_open\"),\n",
- " [Input(\"right_pos_tab1_img_div\", \"n_clicks\"), Input(\"right_pos_tab1_modal_open\", \"n_clicks\"), Input(\"right_pos_tab1_modal_close\", \"n_clicks\")],\n",
- " [State(\"right_pos_tab1_modal\", \"is_open\")],\n",
- ")\n",
- "def toggle_modal(n1, n2, n3, is_open):\n",
- " if n1 or n2 or n3:\n",
- " return not is_open\n",
- " return is_open\n",
- "\n",
- "#####################\n",
- "\n",
- "right_pos_tab2_modal = html.Div(\n",
- " [\n",
- " dbc.Button(\"Vergrößern\", id=\"right_pos_tab2_modal_open\", outline=True, color=\"secondary\", className=\"mr-1\"),\n",
- " dbc.Modal(\n",
- " id=\"right_pos_tab2_modal\",\n",
- " size=\"xl\",\n",
- " children=[\n",
- " dbc.ModalHeader(\"geglättet\"),\n",
- " dbc.ModalBody(\n",
- " children=[\n",
- " html.Img(\n",
- " id=\"right_pos_modal2_img\",\n",
- " src=asset_url + init_date.strftime('%Y_%m_%d') + \"/curve_trend_{0:05d}.png\".format(init_countyid),\n",
- " style={'width':'100%', 'height':'100%'},\n",
- " ),\n",
- " ]\n",
- " ),\n",
- " dbc.ModalFooter(\n",
- " dbc.Button(\"Schließen\", id=\"right_pos_tab2_modal_close\", className=\"ml-auto\")\n",
- " ),\n",
- " ],\n",
- " ),\n",
- " ]\n",
- ")\n",
- "right_pos_tab2 = dbc.Card(\n",
- " outline=True,\n",
- " color=\"light\",\n",
- " className=\"mt-3\",\n",
- " children=[ dbc.CardBody(\n",
- " children=[\n",
- " html.Div(\n",
- " id=\"right_pos_tab2_img_div\",\n",
- " children=[\n",
- " right_pos_tab2_modal,\n",
- " html.Img(\n",
- " id=\"right_pos_tab2_img\",\n",
- " src=asset_url + init_date.strftime('%Y_%m_%d') + \"/curve_trend_{0:05d}.png\".format(init_countyid),\n",
- " style={'width':'100%', 'height':'100%'},\n",
- " ),\n",
- " dbc.Tooltip(\n",
- " \"Analyse und Vorhersage der Infektionszahlen für den ausgewählten Landkreis. \"\n",
- " \"Der Nowcast entspricht der Schätzung der realen aktuellen Neuinfektionen für den angegebenden Tag. \"\n",
- " \"Diese Schätzung korrigiert die gemeldeten Zahlen, die aufgrund von Verzögerungen im Meldeprozess \"\n",
- " \"und einem unbekannten Erkrankungsdatum kleiner als die tatsächlichen Zahlen sein können, auf der Basis einer Vorhersage. \"\n",
- " \"Die Vorhersage nutzt das gleiche Modell um den Verlauf der kommenden 7 Tage, für die noch keine Zahlen vorliegen, vorherzusagen. \"\n",
- " \"Das geglättete Model korrigiert die Ergebnisse bezüglich eines Wochenrhythmusses bei den Meldeverzögerungen (siehe Erklärvideo). \",\n",
- " target=\"right_pos_tab2_img\",\n",
- " style={\"width\": \"200%\"},\n",
- " placement=\"right\",\n",
- " ),\n",
- " ]),\n",
- " ]),\n",
- " ])\n",
- "@app.callback(\n",
- " Output(\"right_pos_tab2_modal\", \"is_open\"),\n",
- " [Input(\"right_pos_tab2_img\", \"n_clicks\"), Input(\"right_pos_tab2_modal_open\", \"n_clicks\"), Input(\"right_pos_tab2_modal_close\", \"n_clicks\")],\n",
- " [State(\"right_pos_tab2_modal\", \"is_open\")],\n",
- ")\n",
- "def toggle_modal(n1, n2, n3, is_open):\n",
- " if n1 or n2 or n3:\n",
- " return not is_open\n",
- " return is_open"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "#####################\n",
- "# County Picker (right)\n",
- "#####################\n",
- "right_pos_controls = dbc.FormGroup(\n",
- " children=[\n",
- " dbc.Label(\n",
- " id='right_pos-label',\n",
- " children=[\"Wähle Landkreis:\"],\n",
- " ),\n",
- " html.Div(\n",
- " children=[\n",
- " dcc.Dropdown(\n",
- " id=\"right_pos-variable\",\n",
- " value=init_countyid,\n",
- " options=[\n",
- " {\"label\": row['LKName'] + \" (\" + row['LKType'] + \")\", \"value\": row['countyID']} for index, row in metadata.iterrows()\n",
- " ]),\n",
- " html.Div(id='right_output-container-pos-variable'), #, style={'display': 'none'}),\n",
- " ]), \n",
- " ])\n",
- "\n",
- "# County Picker\n",
- "@app.callback(\n",
- " Output(component_id='right_output-container-pos-variable', component_property='children'),\n",
- " [Input(component_id='right_pos-variable', component_property='value'),\n",
- " Input(component_id='right_output-container-date-picker', component_property='children')])\n",
- "def update_right_pos_variable(value, assets_dir):\n",
- " if value is not None:\n",
- " return asset_url + assets_dir + \"/\" + \"curve_trend_{0:05d}.png\".format(value)\n",
- "\n",
- "# geglättet\n",
- "@app.callback(\n",
- " Output(component_id='right_pos_tab1_img', component_property='src'),\n",
- " [Input(component_id='right_pos-variable', component_property='value'),\n",
- " Input(component_id='right_output-container-date-picker', component_property='children')])\n",
- "def update_right_pos_tab1_img(value, assets_dir):\n",
- " if value is not None:\n",
- " return asset_url + assets_dir + \"/\" + \"curve_trend_{0:05d}.png\".format(value)\n",
- "@app.callback(\n",
- " Output(component_id='right_pos_modal1_img', component_property='src'),\n",
- " [Input(component_id='right_pos-variable', component_property='value'),\n",
- " Input(component_id='right_output-container-date-picker', component_property='children')])\n",
- "def update_right_pos_modal1_img(value, assets_dir):\n",
- " if value is not None:\n",
- " return asset_url + assets_dir + \"/\" + \"curve_trend_{0:05d}.png\".format(value)\n",
- "\n",
- "# ungeglättet\n",
- "@app.callback(\n",
- " Output(component_id='right_pos_tab2_img', component_property='src'),\n",
- " [Input(component_id='right_pos-variable', component_property='value'),\n",
- " Input(component_id='right_output-container-date-picker', component_property='children')])\n",
- "def update_right_pos_tab2_img(value, assets_dir):\n",
- " if value is not None:\n",
- " return asset_url + assets_dir + \"/\" + \"curve_{0:05d}.png\".format(value)\n",
- "@app.callback(\n",
- " Output(component_id='right_pos_modal2_img', component_property='src'),\n",
- " [Input(component_id='right_pos-variable', component_property='value'),\n",
- " Input(component_id='right_output-container-date-picker', component_property='children')])\n",
- "def update_right_pos_modal2_img(value, assets_dir):\n",
- " if value is not None:\n",
- " return asset_url + assets_dir + \"/\" + \"curve_{0:05d}.png\".format(value)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Define the main body of the webpage \n",
- "https://dash-bootstrap-components.opensource.faculty.ai/docs/components/layout/ \n",
- "Layout in Bootstrap is controlled using the grid system.\n",
- "The Bootstrap grid has **twelve** columns, and **five** responsive tiers (allowing you to specify different behaviours on different screen sizes, see below)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [],
- "source": [
- "#####################\n",
- "# Main Structure\n",
- "#####################\n",
- "tab_height = '5vh'\n",
- "body_layout = dbc.Container(\n",
- " style={\"marginTop\": 100},\n",
- " #fluid=True,\n",
- " children=[\n",
- " \n",
- " #####################\n",
- " # Introduction\n",
- " #####################\n",
- " \n",
- " dbc.Row(\n",
- " children=[\n",
- " dbc.Col(\n",
- " style={\n",
- " \"marginBottom\": 10,\n",
- " \"width\": 12,\n",
- " }, \n",
- " children=[\n",
- " dcc.Markdown(\n",
- " f\"\"\"\n",
- " ##### **Ein Gemeinschaftsprojekt der Arbeitsgruppe [Neuroinformatik an der Universität Osnabrück](https://www.ikw.uni-osnabrueck.de/en/research_groups/neuroinformatics/people/prof_dr_gordon_pipa.html)** \n",
- " ##### **und des [Jülich Supercomputing Centre](https://www.fz-juelich.de/jsc), auf Basis der Daten des [RKI](https://www.rki.de/DE/Content/Infekt/IfSG/Signale/Projekte/Signale_Projekte_node.html;jsessionid=C61DE534E8208B0D69BEAD299FC753F9.internet091)**\n",
- " \"\"\"\n",
- " ),\n",
- " ]), \n",
- " ]),\n",
- " dbc.Row(\n",
- " children=[\n",
- " dbc.Col(\n",
- " width=4,\n",
- " children=[\n",
- " html.Img(\n",
- " src='https://www.ikw.uni-osnabrueck.de/fileadmin/templates_global/public/img/header_logo.gif',\n",
- " height='48', # width='500',\n",
- " style={\n",
- " 'display':'block',\n",
- " 'margin-left': 'auto',\n",
- " 'margin-right': 'auto'\n",
- " },\n",
- " ),\n",
- " ]),\n",
- " dbc.Col(\n",
- " width=4,\n",
- " children=[\n",
- " # html.Img(\n",
- " # src='https://www.rki.de/SiteGlobals/StyleBundles/Bilder/Farbschema_A/logo_a.jpg?__blob=normal&v=7',\n",
- " # height='48', # width='500',\n",
- " # style={\n",
- " # 'display':'block',\n",
- " # 'margin-left': 'auto',\n",
- " # 'margin-right': 'auto'\n",
- " # },\n",
- " # ),\n",
- " ]), \n",
- " dbc.Col(\n",
- " width=4,\n",
- " children=[\n",
- " html.Img(\n",
- " src='https://www.vi-hps.org/cms/upload/logos/full/jsc-logo.png',\n",
- " height='48', # width='500',\n",
- " style={\n",
- " 'display':'block',\n",
- " 'margin-left': 'auto',\n",
- " 'margin-right': 'auto'\n",
- " },\n",
- " ),\n",
- " ]),\n",
- " ]),\n",
- " dbc.Row(\n",
- " children=[\n",
- " dbc.Col(\n",
- " style={\n",
- " \"marginTop\": 30,\n",
- " \"width\": 6,\n",
- " },\n",
- " children=[\n",
- " dcc.Markdown(\n",
- " f\"\"\"\n",
- " -----\n",
- " ##### BSTIM-Covid19 \n",
- " -----\n",
- " Aktuelle Daten und Vorhersage der Neuinfizierungen mit COVID-19 für Landkreise in Deutschland.\n",
- " Das Model beschreibt die zeitliche Entwicklung der Neuinfizierungen in einen Zeitraum von 4 Wochen.\n",
- " Das Model beschreibt dazu nicht nur die wahrscheinlichste Entwicklung oder die mittlere Entwicklung,\n",
- " sondern schätzt die Wahrscheinlichkeit für verschiedene Szenarien ab, die mit der aktuellen Datenlage kompatibel sind.\n",
- " Zudem wir der Interaktionsradius vom Infektionsgeschehen geschätzt und als Interaktionskernel dargestellt.\n",
- " Die Arbeit basiert auf einer Adaption des [BSTIM Models](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0225838#pone.0225838.ref009) angepasst an die COVID-19 Situation.\n",
- " Das Model beschreibt die tagesaktuellen Daten basierend auf den [Daten](https://npgeo-corona-npgeo-de.hub.arcgis.com/datasets/dd4580c810204019a7b8eb3e0b329dd6_0/data?orderBy=Meldedatum) des RKI.\n",
- " \"\"\"\n",
- " ),\n",
- " disclaimer_modal,\n",
- " ]),\n",
- " dbc.Col(\n",
- " style={\n",
- " \"marginTop\": 30,\n",
- " \"width\": 6,\n",
- " },\n",
- " children=[\n",
- " dcc.Markdown(\n",
- " f\"\"\"\n",
- " -----\n",
- " ##### Wie funktioniert die Vorhersage und Analyse\n",
- " -----\n",
- " \"\"\"\n",
- " ),\n",
- " html.Div(\n",
- " style={\n",
- " 'width': '100%',\n",
- " 'float': 'left',\n",
- " 'margin': '0% 0% 5% 0%' # top, right, bottom, left\n",
- " },\n",
- " children=[\n",
- " dash_player.DashPlayer(\n",
- " id='video-player',\n",
- " url='https://youtu.be/8-AfYeosBW8',\n",
- " controls=True,\n",
- " width='100%'\n",
- " ),\n",
- " ]), \n",
- " ]),\n",
- " ]),\n",
- " \n",
- " #####################\n",
- " # Plots Section\n",
- " ##################### \n",
- " \n",
- " dbc.Row(\n",
- " children=[\n",
- " dbc.Col(\n",
- " dbc.Alert(\"Basisauswahl\", color=\"primary\")\n",
- " ),\n",
- " dbc.Col(\n",
- " dbc.Alert(\"Vergleichsauswahl\", color=\"primary\")\n",
- " ),\n",
- " ]\n",
- " ),\n",
- "\n",
- " dbc.Row(\n",
- " children=[\n",
- " \n",
- " ##### left plots\n",
- " dbc.Col(\n",
- " children=[\n",
- " dbc.Card(\n",
- " style={\n",
- " 'margin': '0% 0% 0% 0%', # top, right, bottom, left\n",
- " 'padding': '0',\n",
- " },\n",
- " body=True, \n",
- " children=[\n",
- " \n",
- " # --- Zeitangabe (left) ---\n",
- " dbc.CardHeader(\n",
- " left_date_controls,\n",
- " ),\n",
- " dbc.CardBody(\n",
- " className=\"mt-3\",\n",
- " children=[\n",
- " dbc.Tabs(\n",
- " id=\"left_date-card-tabs\",\n",
- " active_tab=\"tab-0\", \n",
- " children=[\n",
- " dbc.Tab(left_date_tab1, label=\"Infektionen\", style={'padding': '0', 'height': '550px'}),\n",
- " dbc.Tab(left_date_tab2, label=\"Interaktionskernel\", style={'padding': '0', 'height': '550px'}),\n",
- " ]),\n",
- " \n",
- " html.P(\n",
- " id=\"left_pos-card-separator\",\n",
- " className=\"card-text\",\n",
- " ),\n",
- " \n",
- " # --- Ortsangabe (left) ---\n",
- " dbc.Card(\n",
- " style={\n",
- " 'margin': '0% 0% 0% 0%', # top, right, bottom, left\n",
- " 'padding': '0',\n",
- " }, \n",
- " children=[\n",
- " dbc.CardHeader(\n",
- " left_pos_controls,\n",
- " ),\n",
- " dbc.CardBody(\n",
- " className=\"mt-3\",\n",
- " children=[\n",
- " dbc.Tabs(\n",
- " id=\"left_pos-card-tabs\",\n",
- " active_tab=\"tab-0\", \n",
- " children=[\n",
- " dbc.Tab(left_pos_tab1, label=\"geglättet\", style={'padding': '0', 'height': '300px'}),\n",
- " dbc.Tab(left_pos_tab2, label=\"ungeglättet\", style={'padding': '0', 'height': '300px'}),\n",
- " ]),\n",
- "\n",
- " html.P(\n",
- " id=\"left_pos-card-content\",\n",
- " className=\"card-text\",\n",
- " ),\n",
- " ]),\n",
- " ]), \n",
- " ]),\n",
- " ]),\n",
- " ]),\n",
- "\n",
- " ##### right plots\n",
- " dbc.Col(\n",
- " children=[\n",
- " dbc.Card(\n",
- " style={\n",
- " 'margin': '0% 0% 0% 0%', # top, right, bottom, left\n",
- " 'padding': '0',\n",
- " },\n",
- " body=True, \n",
- " children=[\n",
- " \n",
- " # --- Zeitangabe (left) ---\n",
- " dbc.CardHeader(\n",
- " right_date_controls,\n",
- " ),\n",
- " dbc.CardBody(\n",
- " className=\"mt-3\",\n",
- " children=[\n",
- " dbc.Tabs(\n",
- " id=\"right_date-card-tabs\",\n",
- " active_tab=\"tab-0\",\n",
- " children=[\n",
- " dbc.Tab(right_date_tab1, label=\"Infektionen\", style={'padding': '0', 'height': '550px'}),\n",
- " dbc.Tab(right_date_tab2, label=\"Interaktionskernel\", style={'padding': '0', 'height': '550px'}),\n",
- " ]),\n",
- " \n",
- " html.P(\n",
- " id=\"right_pos-card-separator\",\n",
- " className=\"card-text\",\n",
- " ),\n",
- " \n",
- " # --- Ortsangabe (left) ---\n",
- " dbc.Card(\n",
- " style={\n",
- " 'margin': '0% 0% 0% 0%', # top, right, bottom, left\n",
- " 'padding': '0',\n",
- " }, \n",
- " children=[\n",
- " dbc.CardHeader(\n",
- " right_pos_controls,\n",
- " ),\n",
- " dbc.CardBody(\n",
- " className=\"mt-3\",\n",
- " children=[\n",
- " dbc.Tabs(\n",
- " id=\"right_pos-card-tabs\",\n",
- " active_tab=\"tab-0\", \n",
- " children=[\n",
- " dbc.Tab(right_pos_tab1, label=\"geglättet\", style={'padding': '0', 'height': '300px'}),\n",
- " dbc.Tab(right_pos_tab2, label=\"ungeglättet\", style={'padding': '0', 'height': '300px'}),\n",
- " ]),\n",
- "\n",
- " html.P(\n",
- " id=\"right_pos-card-content\",\n",
- " className=\"card-text\",\n",
- " ),\n",
- " ]),\n",
- " ]), \n",
- " ]),\n",
- " ]),\n",
- " ]),\n",
- " ]),\n",
- " ])\n",
- "\n",
- "app.layout = html.Div([navbar, body_layout, navbar_footer])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [],
- "source": [
- "# multipage example: https://yadi.sk/d/JnM7BvKbJp3EdA\n",
- "#@app.callback(Output('page-content', 'children'),\n",
- "# [Input('url', 'pathname')])\n",
- "#def display_page(pathname):\n",
- "# if pathname == '/apps/app1':\n",
- "# return app1.layout()\n",
- "# else:\n",
- "# return app1.layout()\n",
- "#\n",
- "#@app.server.route('/static/<path:path>')\n",
- "#def static_file(path):\n",
- "# static_folder = os.path.join(os.getcwd(), 'static')\n",
- "# return send_from_directory(static_folder, path)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Start the app"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dash app running on https://jupyter-jsc.fz-juelich.de/user/j.goebbert@fz-juelich.de/juwels_login/proxy/8050/\n"
- ]
- }
- ],
- "source": [
- "app.run_server(mode=\"external\")\n",
- "# mode=\"jupyterlab\" -> will open the app in a tab in JupyterLab\n",
- "# mode=\"inline\" -> will open the app below this cell\n",
- "# mode=\"external\" -> will displays a URL that you can click on to open the app in a browser tab"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "--------------------------\n",
- "**Attention** \n",
- "If you get the error \"adress in use\" this can also be the case because simply your layout has an error so that a dash-app could not been started. Open the app in a new browser-tab with the url\n",
- "`<base-url>/proxy/<port>` where \\<base-url\\> derives from the url of your jupyterlab and \\<port\\> is by default 8050. \n",
- "For example: `https://jupyter-jsc.fz-juelich.de/user/j.goebbert@fz-juelich.de/jureca_login/proxy/8050` \n",
- "This will show the full error log.\n",
- "\n",
- "--------------------------"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Show the Dash Flask server is listening"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "!echo \"COMMAND PID USER FD TYPE DEVICE SIZE/OFF NODE NAME\"\n",
- "!lsof -i -P -n | grep LISTEN"
- ]
- }
- ],
- "metadata": {
- "jupytext": {
- "formats": "ipynb,py:percent"
- },
- "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": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/003-Communities/README.md b/003-Communities/README.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/01-Introduction/01-JSC.ipynb b/01-Introduction/01-JSC.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3eb084c1a91c71c8d1cc2fa3baad48e34db252cc
--- /dev/null
+++ b/01-Introduction/01-JSC.ipynb
@@ -0,0 +1,116 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "f66d472c-49c1-47d2-8d28-99749aa5c770.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "ca8705ef-351e-411b-b988-6658cc06a450",
+ "metadata": {},
+ "source": [
+ "\n",
+ "<h5 style=\"text-align: right\">Author: <a href=\"mailto:t.kreuzer@fz-juelich.de?subject=Jupyter-JSC%20documentation\">Tim Kreuzer</a></h5> \n",
+ "<h5><a href=\"../index.ipynb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">JSC</h1>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d510e4f8-4eaf-4261-a15e-358a6646a2ea",
+ "metadata": {
+ "tags": [],
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "# Overview"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e0f3e32-6fe8-424c-80db-b2fbf8f90721",
+ "metadata": {},
+ "source": [
+ "- [About JSC](https://fz-juelich.de/ias/jsc/EN/AboutUs/Profile/profile.html) \n",
+ "- [User Portal JuDoor](https://fz-juelich.de/ias/jsc/EN/Expertise/Supercomputers/NewUsageModel/JuDoor.html) \n",
+ "- [Usage Model](https://fz-juelich.de/ias/jsc/EN/Expertise/Supercomputers/NewUsageModel/NewUsageModel_node.html)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7a7af86c-eeab-42f0-a4bc-7f247061882d",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# Systems"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fa1151a4-609d-40b0-9e30-f2211012a535",
+ "metadata": {},
+ "source": [
+ "## HPC-Systems\n",
+ "- [JUWELS](https://fz-juelich.de/ias/jsc/EN/Expertise/Supercomputers/JUWELS/JUWELS_node.html) \n",
+ " - [User documentation](https://apps.fz-juelich.de/jsc/hps/juwels/index.html)\n",
+ "- [JURECA](https://fz-juelich.de/ias/jsc/EN/Expertise/Supercomputers/JURECA/JURECA_node.html) \n",
+ " - [User documentation](https://apps.fz-juelich.de/jsc/hps/jureca/index.html)\n",
+ "- [JUSUF](https://fz-juelich.de/ias/jsc/EN/Expertise/Supercomputers/JUSUF/JUSUF_node.html) \n",
+ " - [User documentation](https://apps.fz-juelich.de/jsc/hps/jusuf/index.html) \n",
+ "- [DEEP](https://fz-juelich.de/ias/jsc/EN/Expertise/Supercomputers/DEEP-EST/_node.html) \n",
+ " - [Project](https://www.deep-projects.eu/) \n",
+ " - [User documentation](https://deeptrac.zam.kfa-juelich.de:8443/trac/wiki/Public/User_Guide) \n",
+ "- HDFML\n",
+ "\n",
+ "## Cloud-Systems\n",
+ "- [HDF-Cloud](https://www.fz-juelich.de/ias/jsc/EN/Expertise/SciCloudServices/HDFCloud/_node.html)\n",
+ " - [Jupyter-JSC configuration on HDF-Cloud](https://docs.jupyter-jsc.fz-juelich.de/github/kreuzert/jupyter-jsc-notebooks/blob/documentation/02-Configuration/details/HDFCloud.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6c71b6b4-9c25-49a1-97d1-0639dcddf0c4",
+ "metadata": {},
+ "source": [
+ "# File-Systems"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6006d25-132c-448c-b8b5-b35d4fdcc28d",
+ "metadata": {},
+ "source": [
+ "- [HPC-Systems](https://apps.fz-juelich.de/jsc/hps/just/filesystems.html) \n",
+ "- Cloud-Systems: Hosted centralized as NFS server. Each service will mount specific user directory. "
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.8.10 64-bit",
+ "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"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/01-Introduction/02-Jupyter-JSC.ipynb b/01-Introduction/02-Jupyter-JSC.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..0415d1b74c8df5a05b31b348d773460c2319dbbc
--- /dev/null
+++ b/01-Introduction/02-Jupyter-JSC.ipynb
@@ -0,0 +1,144 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "f66d472c-49c1-47d2-8d28-99749aa5c770.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "ca8705ef-351e-411b-b988-6658cc06a450",
+ "metadata": {},
+ "source": [
+ "\n",
+ "<h5 style=\"text-align: right\">Author: <a href=\"mailto:t.kreuzer@fz-juelich.de?subject=Jupyter-JSC%20documentation\">Tim Kreuzer</a></h5> \n",
+ "<h5><a href=\"../index.ipynb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">Jupyter-JSC</h1> "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d510e4f8-4eaf-4261-a15e-358a6646a2ea",
+ "metadata": {
+ "tags": [],
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "# About"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "469fe0ed-79f3-412e-b1e9-c1dbf7f8ae99",
+ "metadata": {},
+ "source": [
+ "Jupyter-JSC is designed to provide the rich high performance computing (HPC) ecosystem to the world's most popular software: web browsers. JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data. JupyterLab is flexible to support a wide range of workflows in data science, scientific computing, and machine learning."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "46238f4c-42ca-4dcc-b6cc-b0ae5ae8b61c",
+ "metadata": {},
+ "source": [
+ "# Setup"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "79c71dd6-9279-44e7-86fb-307a511dad74",
+ "metadata": {},
+ "source": [
+ "## Webservice\n",
+ "Jupyter-JSC is a customized [JupyterHub](https://jupyter.org/hub) running on the [HDF-Cloud](https://www.fz-juelich.de/ias/jsc/EN/Expertise/SciCloudServices/HDFCloud/_node.html). It is deployed in a kubernetes cluster. \n",
+ "There are four basic components to serve Jupyter-JSC:\n",
+ " - JuypterHub \n",
+ " - Interacts with the user. Handles authentication and communication between JupyterHub and the user's services.\n",
+ " - Backend \n",
+ " - Starting / stopping services for the user on any system. Called by JupyterHub [Spawner](https://jupyterhub.readthedocs.io/en/stable/reference/spawners.html). Communicating with [UNICORE](https://unicore.eu) and UserLab Manager.\n",
+ " - Tunneling \n",
+ " - Secure port forwarding between JupyterHub and the user's service. Required to reach compute nodes without access to the internet.\n",
+ " - UserLab Manager \n",
+ " - Starting / stopping services on the cloud systems, where [UNICORE](https://unicore.eu) is not an option. "
+ ]
+ },
+ {
+ "attachments": {
+ "f0958653-c22c-436d-88b3-2afd3f02d4eb.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "19ccfc9d-c95d-4f60-887e-2fba9c63f8ac",
+ "metadata": {},
+ "source": [
+ "## Authentication\n",
+ "If you visit Jupyter-JSC and hit the Login button, you will be redirected to our authentication service. This webservice, called [Unity-IdM](https://www.unity-idm.eu), is connected to the JSC WebLDAP and the [Helmholtz AAI](https://aai.helmholtz.de/). You can use your JSC account, also used at [JuDoor](https://judoor.fz-juelich.de/), [GitLab](https://gitlab.jsc.fz-juelich.de/) and more, or the Helmholtz AAI, to sign in with your local identity provider (IdP). After your successful login, you will be redirected to Jupyter-JSC. \n",
+ " \n",
+ "### [2-Factor-Authorization](https://docs.jupyter-jsc.fz-juelich.de/github/kreuzert/jupyter-jsc-notebooks/blob/documentation/02-Configuration/2-Factor-Authentication.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "db1e5246-f784-48fe-ab50-696f7b56ab2f",
+ "metadata": {},
+ "source": [
+ "## Authorization\n",
+ "### System access\n",
+ "Jupyter-JSC gets the information about the users HPC accounts and projects directly from JuDoor. \n",
+ "While the cloud systems are available for everyone, the resources and quotas may differ for each virtual organization. Both are also dependent on the use of the system and may change. You can see your current quota while your Service is starting. \n",
+ "### Virtual organizations\n",
+ "Jupyter-JSC supports multiple virtual organizations (VO) in Jupyter-JSC. This allows us to offer specific resources and quotas for different communities or workshops. If you have multiple VOs you can choose your active VO in the top right corner of the website. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4be179c9-0210-4788-9128-2fd2f5a2c9fc",
+ "metadata": {},
+ "source": [
+ "## Service Start\n",
+ "Jupyter-JSC uses [UNICORE](https://unicore.eu) to start jobs on the HPC-Systems. These jobs contain all the information to start your service. \n",
+ "On Cloud-Systems, where [UNICORE](https://unicore.eu) is not available, Jupyter-JSC uses an internal solution (UserLab Manager) to start a [Kubernetes Pod](https://kubernetes.io/de/docs/concepts/workloads/pods/) for each of the users services. \n",
+ "During the startup process of the users service, Jupyter-JSC creates a secure ssh-connection between JupyterHub and the users service. "
+ ]
+ },
+ {
+ "attachments": {
+ "697396c6-a739-4884-bdc7-e4877e196bee.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "eaa4d91e-6161-446f-96c7-4daebf9f796a",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.8.10 64-bit",
+ "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"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/01-Introduction/03-Using-Jupyter-JSC.ipynb b/01-Introduction/03-Using-Jupyter-JSC.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ad04c2c73f70609ee50ddcf38d88f3733aa416af
--- /dev/null
+++ b/01-Introduction/03-Using-Jupyter-JSC.ipynb
@@ -0,0 +1,235 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "b8bcaeee-0217-42ea-ab25-2413682bf881.png": {
+ "image/png": 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ptQ4vHtqbDrHevbmv3pHKulqyzuti497DbE1NNxzZ85BjJ1wLsrSKCKlXTGtE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\npxwCSHvn8X1d//TClTZtWSpENUaUht2HfmX3oV+xWix0jG1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63l3auwN92scy55uNXBff\nAy+bFT9vLw5m5jL+2emV+gny92H+Sw/Su32LSuc8nH8uyK9C5z89P9AmjRcNIePOtg+nyyTt57PJ\nw4IAHx+CA/xcLWMarWgeEfPMjGeu3ni28/Dg4WKgtV4H9D9f/ZfaHXy4ZC3//vDr8kSwi4m3zUr/\nTi15fNwVjOzb6bxW8TkXSu0OvGzWOhnYBzJysVqMSnJNFfqyWqv0ToE7JvRARi5eNgvNo8IqGASn\nkp6Vh5fNQligf6W+lNLYnZUz3O1OF1KIcvH/U9l9MING4cEE+HqXL3RO/TxMU+E0zUqe8jKHE6th\nYBgSrTVPvT2X/36+gtn/up+rBrpjD12mYtyzb/PtDzuZ+0JCueYqHNclzcjBYkiiQoIqGHvgDp+o\nKtShqs+jqKSM9Ow8osOCCPavvPljKoXDZVYw5k/cF0OKKg1Tl6lwnfa+naaJUrrGmOSC4lJyC4pp\nEhGC1WLgcLnfw6kJRFV9HkrrSuoEJ+7BuSR65RWWMOj+f/PQTZdy7zVDz7qfqjick4/NaqlQ5cuD\nh/rmgizjtr3/f2uB+Hbj/jXIZpHPeFktlwrO3UNbR8oKS0rfPVZUOunA3GdrF/H34OEPiI+XjT+P\njufS3h35aOlavlj9A7sPZmA/h5jIM0UAvt5e9OnQgruvimNE305EBAdUa7A1BM7EgDgRI3m2fXnb\nrLRrVkmuuxI1Jb9IKaocpybDq+0pY1a1oDAMWWWsdQXDVgiEECilySsoJuPoMaQQlNqduEyFYchK\nUoxWi1FjQp8hZZ3vv7+vd5UJeRX6slV+DzXdF4shK3nyrIZRfRDZcQL9fCqoWFSVAV/VuCeSkirN\nuwHLblW3aPPgoT6p/1+IoUMthu44SivRXmhyTYtcxqq3KkS0d71nUgtpusYagtuklO3q0u2ZemQF\nYpMWfKSk42NWTT8jXVgPHhoa59sjezoZuceYu3oTHy1ZWyE55XwR7O/L5f07c21cT0YN6EqgJ9bs\nd8dP+w9z2cOvUuZw0LZpNBZDUlBcyk/7D3P9kF6899RdlTLdPZx/zqdH1oOHC0H9GrKdbrLJ0Ig3\nNeJUMUS7BfMZZ8q0KqPaO93+fF+rNEZJqYdIYfQVgiqj5utgyGag+UYIvjVNltephKoHD78RLrQh\n6x4TXMrkwJFcPl2xntWbd7LrUCaHs/POuf64zWqhSUQIPds1Z2Tfzowe1J3wYP8GG0LgoX44lHWU\nBWu2su9wNk7TJNDXh8Fd2xDXrU2D9iz+nvEYsh5+69SrIWvETxintLinilM5OiVpDIgaf/2GJiZa\ncn72aWMVZictRSeEbi6F8NcCP9Olgn785XChEBSiRT7oQhB7pGC3y+naxbppZxdA68HDb4CLYcie\nTl5hMenZeew7nMPaH/ewassuso4WYHe6cDhdOFymW5f2uK6llAJDSqwWA6vFICzQny6tmtC5ZWO6\ntW5Ky5gImkaFerxwHjxcRPKLShj24MskXD+Mu6+Kr/0CDx4aGPVqyMq4hJc1VFlmQwt9+4mytr8n\njPgJ47QiSGFdiHTFkjw5mX4PRWF1BRlCd9XIcIE+YqrSb6TwmSAQeaZUiw3NcI30lug9rpTJK434\nhDu01qHKsL2PXUhp2G8RQhaYKUkzjbiEe8yjER+yPdEh4ybep1KSpsm4ifehlVSSLShtxyJ2QWQZ\nKquvoWRjDW0FZJtCzMM0GxuGbmxq1uIlJQ4ZhdL5UqhrhZCZZkqSW7smfmILpHAaSg8yc8PnEZId\ni9NyjPVvZhI/YSB2Yxuh2ClW/dBKSIwOSoj1SPshTK9IUt76iSEPxCGcedKUQwGUUfIOq2aUET+h\nqVTiJiEoME1zvsVqdNOmaAE4zdSk9y3xE4cpTVuFudYiRZhrdeRq4jLjwNyNsHnj0sqwMFQr7aeE\na54Fa0etRSvQpabFukCarr+gdZkSzjmkTD9y8Z6I80NDMGSrwmUqDufkkZVXQHZ+ES5ToY4nznjb\n3Jn4wf4+RIQEEhroV2XmsQcPHi4eDqeLpRu20aF5DK2bRNZ+gQcPDYx63sdT1YvvS1th/Y7VEEiU\nWokmKnXKVKQKk1p3BcDLGWoxdGMtRJRKSZqmoQVWbz8hxF4zNWk6yVMOaSH8VErSNKXpxpAJfUzY\np0pcb1tcjv7SYh+r8iLfMTEPEf9ADy1EEyMs53JG/NVPoG8FBEIbKnXKFKlFX0MarXEoX3xyDUOL\njmZq0hxQeWZq0nSUthoWWmotW0kh78CugizabCaFvkWlTpkqtDjG0AnHU0pVI0wVqbVuaYRm326R\nogk+pjtzxOm1WVrVnbLYvBNpTZNa9lIpSdOkVkPAEmzRZjMAaZpdDJcRq0yxQKGTLdrPrVShRYSS\n+jvTsH5uGPIKpWlrpiZNRyoHA++LVFp1UylJ0yRymEsLk/jcbhKjqyFslxhaXYqvM1+jw5Ulcjra\nK1KhO5ipSdPN1MkfoRwBQrBTNcqaLIX1+gv/HPxxsRiSZlFh9G7fglH9uzB6UDeuievBTZf0ZvSg\n7lzSsz092janSUSIx4j14KEBYrNaGD2ou8eI9fCbpV4NWYmxsqrjQuhNv08R+0SlDLlWxk94FNNV\nYX/UZUqN1qHG4Am3orUDQGt9mRE3cTwAmpYyfmKikHq/RclQFBlsml7iSp2yBC0k2xMdCOthi1Dh\naDI0qpFRVnaVFnrxieuN+IQ7BOJQhUKQoprwDYFTIdcaGENOTJH4CU2UNHsDp2uj5KPJVUqdjHVe\n93qpEBwQyPzjn6XrRL84jEpjSoOxUou7XF4+a8uPaa6RpvNxU7AbrW0yLuF+rUVHGufmIoSjvD8Z\nsdZQuo/QuhitfTXCworpx6TgB6lyHgaXBYTFGDzxLwxO6Oe+HYyURyJeVej5tXxoHjx48ODBg4ff\nCfVqyLpSktZIof4HolyzR6C3qWLXi/U5ToNh6MPBUpndlFPNkobsJTQHGJIwwFAyDtO1ByGOmqlT\nPlWpU6a4L9BppjbXMvRObxD7lVSvaC0jXabeKaW+hMEJ/Yy4ieOF1LkMfqCb1Gq4S+udAEJxWENT\ntHQvCAS/mMmTPzBTkuaaWu00LJZLjEJ1mSn0T9XON+WtDVrS/Pj1TiReKOGHsgSc3tRMnTwPIfpW\nOGbqA6ZS7lhkoYMY+kATtPDDbj+iEN2In9BBHC+Jq4SYhZT7Tr1ewEal5WeGkp1BulTK5KlAEXPm\nmGjCiJ/QFIQPqxJdWuh2pjz+3rUoc3ttaauU8ZkURh8Qpom5AixH3G1YLgSpSFl7iR0PHjx48ODB\nw++Cek8RNpOnfqINy00WzL9pre5WKZMfZNPvVP5q1Rv5ymCJxWZ0VH7yHTN1ygKQFlOIVNZN+1U5\nzS/L22ZHHpUYuy1YWoLNXxliHqumFCnDtYS1kw8oSLYY2tdMSZppJk/52GKYoUqZ35I85ZBSzDdL\nXUuU0/WpcrkWALpC3ylT00zEbtPQmayevA5AmeorAPLDc0xtpCiXOR9AeXtPcpleG1R01nTDKdso\nzSychlsezebajirdU97WcP6VQsfB8nFs7MXb9SOAsokphkt1VyWOJDZNL1EWc7ZFiCZm6pSPTIvz\nO2TREWWWvEeh6fZUq9I9Qhv5CLzMRpnvKkPMA1BKfECnRJsynFMMrbsqp3UygNJqMjJyg4n8ysT8\nkrXTspSpV1mEq406Gv6eMvV8C5aWCEcL/IwsE7HGTIn8ApfRcGutevDgwYMHDx7qlYarNO7Bg4dy\nGmqylwcPHjx48HAx+X8Xq1zYoK5w/QAAAABJRU5ErkJggg==\n"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "523d0908-4d97-424e-affd-182aa4fabc87",
+ "metadata": {},
+ "source": [
+ "\n",
+ "<h5 style=\"text-align: right\">Author: <a href=\"mailto:a.grosch@fz-juelich.de?subject=Jupyter-JSC%20documentation\">Alice Grosch</a></h5> \n",
+ "<h5><a href=\"../index.ipynb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">Using Jupyter-JSC</h1>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cb23e3f7-48b0-457a-9392-abc60247b501",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "[Overview](#Overview) \n",
+ "[Starting a new JupyterLab](#Starting-a-new-JupyterLab) \n",
+ "[Starting or modifying existing JupyterLab configurations](#Starting-or-modifying-existing-JupyterLab-configurations) \n",
+ "[The spawn page](#The-spawn-page)"
+ ]
+ },
+ {
+ "attachments": {
+ "f15e7b20-1096-4341-8999-6cc125b048d8.png": {
+ "image/png": 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44hq7+dUv7NXhUyw3Feeycy0KgsJze7S3V284yypBcLijbLZPb+dylFuxH8Vr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\nxswRdQSDzz+7e3fcGEO8Q4Pw+P0NrnCHdmiu3cB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zRDFrPHJw2D/s0N54RKyxZPA8qs8R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\n7mqIeg2tW17r3KKx262bdx3w2xvcsg3b3cR+nWx5uWVcfLB8/XZPp4N+7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ftFw/Fa+7ov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jwiCORt/WA5f6+LTCSnMj+Kj3TfU86o+Fcn0qrT6hmhdaD09rT2n1s17n+LTS4D4JkAAJkAAJ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\nAggggAACCCCAAAIIXCwQY/++vecTfzpQYo/xfenmjXK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KWVrpOfvfovlzYmWJ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N59Jm89V1NytBsin05cIE0rFZHHzX9SEgIIIIBA5Aks2bJfKhfOFaED3EvdCB2QSvcs8FqX\nrlKzVg0Z1D98b0PffrePzJ+3QFavXHbPfXjYG2jRooVUqVJV/Pz8HvZTjdTza9KkiZQoWZLpFCNV\nmcYRQACBqBWoXbuOrF27Vvbv3y+5crnefy5e7Bp978knn5Q///zTjuKUOHFi27EVK1baZeUqVdwd\nLV++grzbp497O6IrM6ZPt1V/m/iblCvv+k6iVevW0rhxY2nYoIH8NnEiwX4RxaUeAggg4CHQuk0b\n0UdEkgafNHv6aSldOmI3rEXkmNRBAAEEHkWBqAj+fhRdOWcEEEAgsgV0RL/gAv3qly9oDz1zxTav\nLuhIf1pnaGfvUc+8CrGBQBQIPFmpqJy7dEWWbtwT4aO9WL2UDfTbcfi4FDCjVzrpzedqy6evNJEm\nlYs5WRI7Vmz3+r2sxPHxJrZ7OQZ1EQhN4P78Sw7mCOcuX7O58ePG8dr7VvMa0uaJMpI2uesL+3MX\n/5PdB49L0TxZvAL9tJLWndD9WXmibH45fd636R28DsYGAggggEC4BBoP/kk6fzE5XHW0cKfPJ4nW\nJcUsgbFjvpN5c+fLO336+txxJ9BP65LCFpg/b7680eV1OwqFU1qnJ6xUsYIZzWiWkyVDhgy2eTod\nogY96H59HD161JbZvHmzO2/r1q027+DBg+68ffv22bypU6faPJ0aq3ixorbdmzdvuo8TdEVHHBww\nYICdZljr6DS8zzZvLlu2bAlaNELbY8eMkWZNm9gpsQoXKijdu3UTnYbRM+m5PVG3ri2j5zxz5kx5\n5ulm9jx0SkVNS5cts446PaQmnWrMMdK+Nm7UyNbXc/75J65FFok/CCCAQDQXqGxeDzVpwJ+T5s2b\na0fX6/zqqzZrxYrlzi5ZtGih5MyZ0z7cmfdp5d9//7Ut+SVI4NVizZq15IMPP5TixYt75bOBAAII\nPIoCGzdutO+79TND1qxZ5OU2rUWn2fVMy5YuFZ2iXcvoVOh//PGHfd8+PTCoeswPP9jtK1euiH4W\n0ff0E01AdefOnWyb2m63rl1F93smvYlKU4f27Ww9z31B133pp35OqV69mvvzj/9R7/PQNufPnyc1\na9Zwf5bRkQWjy4jtQc+ZbQQQePQEZs362353otfbBvXry3ejR4szyrZqhLZfv0/S72q0rlN/9erV\nFvGbr7+Wr7/+yq7XMtfAwYNd31c513G7w/zRa6Rew3VUbp32V6/deg3X75Y0/8dx45yiLBFAAAEE\nokDg0PEzMvKPxcEe6fcPO4g+gktaR+uSEHiQAtP6d5QlZlQ+nVo3Y4bUEepK5rTJbb2D/t7/nj+b\nMEfW7zks+/xPiY7Et2JkT0mayE8SxI8nW8f0lfoVCtt6OhXvxu96y+W/h0vAgi/lyG8Dvabn/V/X\nZ2XT9+/K9EGd5ca8/8nunz6QWUNc319WKZrHTh+cOGXSCPWdSghEVCDSgv1eqFhI5FaA5OzwiQwc\nP1c2mehwTTpE5mcdG0vtkvns9prdh+3yqcCocrvh8adKkdzyy9svSqUIjjTl0RSrCCCAAAJhCCzs\n104mrNwWroA/DfT7ddV20bqkmCWQ//HHJTwBf56BflqXFLbA8RPHbRDD2rVr3IX1i1ANbDhz5qw7\n79ChQzZv165dkj17djl0+LDdXrhggS2zYMH8wHbWyuLFi2zevLlzbZ6WzZEjh4z/5RcTqPeMzdMC\nO3bskPffe086dXrFlg/uj/7Y1u+jD0WnxdIpFS9evChTp/4hLZ5/7q4f2IKrH1qeBjB27NhBZsyY\nYYvt2bNHRo78UsqVLeMO+Dts+l6mdClZuNB1nurStMlTdjQnXT937ryte+rkSXteK1eusNv645/u\n10cN8yOdfomtSc+5bduXZf369XabPwgggAAC0VegbNlytnOrVrpG7NONmeY1o5EJ4Hb2adC8pmvX\nrsniRYukTp26dtv5cyvglty4ceOuR2iB7k5dz2W9+vXsZp3ateQjE9y3csUK0TZixYolb7/9jrR4\n4QXP4qwjgAACj5yAvs/W9/HLly+Tbt27S6uWreQX8/lDpzg/f+6c9dAbkDQ4bvLkyfLyy20lT548\n8tyzze179jOnT9syR/397bYGpOjUufp+vuVLL9oRVJs/01zy5s0rX301UgaaG5I8Uz7z+VOn8NV+\nfPrpJ567vNZ96efvv/9uP6f4xfeTd8zNRIcPH5Ivv/yfVzt6A1L9evVk9+7d8tZbb4uOKjtgQH9p\nZz5rkBBAAIEHLaA3RupNjwsXLpQOHTpK3LhxzZS7nW3wtPYtrP3PP/es+W5psa3bpcsbsnnLZqlS\nuZKcNtfqPHnz2GuxtqMjcVesWFH0u6ofvve+6Xfc2LH2Gl64cGHp36+fvXY3MCNi9w+8frc3wdl/\n/fWXNkNCAAEEEIgCgRG/LwzxKM3eHy36CCmFVjekOuQjEBkCbZ4oL9tH95b3WjeQ2EkShusQc9ft\nsuXrlStoA/reefEJyZ8rs80r2X6wFGzVTzbvOSI3bt5yt3vNfKeoIxp/0KahfGQehU15/zMX5MLl\nq5IlbQqb91QV14iA+bNlkCJmf4PyhewIgrkzp5Vr12/YtrSN6zduyeVrrm33AVhBIJIFIi3Yb3Db\nhtK0SlG5cfGKnYa3+ptfSqrm70vrT8fLsTOuu+b13NbsOmJPMT9T9EbyU03zCCCAQNgCRXJlskF7\nvgb8eQb6aV1SzBPwNeCPQL+oe27jxIkjTz31lD3gnDmz7dIJ+tONRQtdwX6zZ7v2aVkNSOj1Vi9b\nVr9c3W+C90aOdN2JrXdT6xezwaVhw4ba7J49e8mp02dk1+49dvSi0uZHO2e0wODqhZWnwXYaaKip\nceOnZNHiJe4vfDWw0PnxbthQ1/G13K9m6sTtO3ZK3bpP6KbPqUjRojY4cl5gQIhW9Awc8bkhCiKA\nAAIIRKmATs9bsVIldxC7BlToiLY1atSURIkSmYCRWib4e5rtk47SpKl6jRp26fzREUySJkl81yNJ\n4kROEZ+W1avXkB9+GGOD3gcOHCDVqlU1dxKnlxdfaEEAuU+CFEIAgYdd4IP337enuGLlKund+137\n3n7suB9Fb8r54osv7L733+trl1u3bZchH38s4378SZo1C3tKsLTp0smBg4fkM/PZYObMvyRz5szu\nm3k8XV977XX7WUWDsrdv3+65y73uSz87dmgvGpwy3QSYd+/+psybv8Ae02nk9u3b8mb3bnZz69Zt\n8qEZ0U8/W/Xo0dMG0mzYsMEpyhIBBBB4IAKvv/aaHQ173/4DMmDgQJliRlHVkaj7vNvbfj8U2n4N\n6NPrmAZla92+5rubsSZwT9976/c1+l5cH5reetsEO1euLC+++JK9kfPsWddNq3qj6IQJE6Rtu3b2\nfbuO8qfX1a+//sZeK3//fYp9n3/61KkH4sNBEUAAgUdRYP6mvcGfduIEcvbSFfsIvoDIws0h1A2p\nAvkIRKJAChPk96EJ9ttngv50el9f0+SF6+XzwKDXcgVyysB2jWS7GYnvxJQh8k2PFu5mqnQZKhev\nXJOrJlBPgwD/XrlVWpsgQ02dh0+QPC+8Lykbvimz17g+c5bOl91dV1dW7zwoWVu8J5VNO436jrL7\nlm7ZK6U6DBa5ctWrLBsIRLZApAX76RzVo7s1l03f9DRRr/WkdH7zH8HcsTlt2VYp+Now90h/fvHj\n2nO8QqRrZD/XtI8AAgj4JOBrwB+Bfj5xxohCYQX8EegX9U9j/foN7EF1VLyrV6963Q2tX6LqFCk6\nAp+mhg0b2i9k9Yc2TVWrVTN3Y5+R4iVK2G39s9xMgxtcypotm83+5JOPRafZHfnllzLITNHy7bej\n7Re1wdXxJc+ZflfL6o9/ZcuWtV/46pfHmqZNm2qXa9a4pompUrWqCQpsLLly5ZKu3brafb7+6du3\nr6RPn8F+kaw/DGo6Gzi6iK9tUA4BBBBAIAoETPBE0FSzZk07dbz+YLjIjEyiqYr5QVFT7Tq1RUeF\n1R8JnZFdK5ngQM+UP39+efPNHnc9NBAlvOl5M0XkyVOnZeJvk6R9hw6SIGFCmTRpklQoX050FCgS\nAggg8KgKaPCbfgbRm4oee+wxN0PdOnXs+rLAzxorzKioOpqfZ5mGDZ90lw9ppb6ZfjJJkiR2t5+f\nnxQqVFguXbp0V3EdueqbUd/a/E6vdPSarlIzfenn8ePHbGB3y1at7EhYWk8DzFu3bqOrNunnKg14\nKV+hgqxbt9aeu55/ipQp7P7169YFlmSBAAIIRL2Afkeko5i2MdetZMmS2Q7ENr/F/fXX37Jt+w4b\n7Bfa/jRp0tgA5xEjhouOvjd16lQpXbqMzDAjmpYuXTrYE3ru+edt/ozAKdmdEfueD8wvWqyYfU+v\n0wn/8P33ZsKvW6I3ZL7UsmWw7ZGJAAIIIHD/BXYecM2wGLTl9CZwakzPF+SDlq4ZDYLu1+3tgbMz\nBrePPAQelEAOM52vTu87f5jvvxe98flEKdFxiAz+Zbas3XVIbppZSNOZqXU7NKwk28aam9MSJQj2\ndCp1GyZ13/qfbNp3VNrULy+fdGoquTKltWUT+sXzqjNq+jL5x/+0LN+yzyufDQQehIAr0i4Sjnzp\nv2sSP15cyZouhbzWqJJ9XLt+Uz76ZY58NXWp9DfL3/q0khK5XT/Kbj98Qp6q6JoT27M7AeYLpRXb\nDkqpfFnFz7RHQgABBBCIfAEn4K9aX9fQ3iNf974bn0C/yH8OovoITsBfq9Zt5R1z8EH9+9kuEOgX\nic+ER9CDTkHomaqa4DdNGvygU+1q0ru0nS91nTzNr1KlqixbtlRXberVs6ez6l76HzvmXvdcGTBg\noJme6gl7HA2o0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CKBHGY6X53ed8GG3VKj23CfjvrG5xNlzKxV0rxq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\ngACBEypQrQ7FxvXrYmJyUoW/EyrfeVmp6FeCfmUtjnUrY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QIECBAgQIAAAQIE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\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W2/IeessvxdQl26NUHyz3De3ZFRv/5N294+x5qPPlWBpNXn5FTF72hNYYRv42K+91h/0y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\nLfPAfdZ26aeqXXj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X6NjJ+N7/9nwuvbn8t6heqDd6TQ9hnqzQ3xc1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\ns2O0xOxRCv4dGB+o+LeivIRx9YoJhdLelOq0n/S3miJyjYY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/U8ixMgvemNxp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AABAgQIECBAgAABAgQIECBAgAABAgQIECBA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\nwvQVpUK/K0oUlVY3VpNqFa+VwoULy/8BJZpwNGHk224AAAAASUVORK5CYII=\n"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "d427d5ac-0135-46c0-96f2-38b9495d1676",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "___\n",
+ "## Overview\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "attachments": {
+ "c0f4b2d4-c982-4d6f-8d45-f864126a4799.png": {
+ "image/png": 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aaNMtZINNNg8MzYs/jeWTTopGmmI/Ms/D/87nOsPIIJXyTXlC8hN2XmUJ41c5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\n+Ctt8vzX+utJA/Emk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aNeeb1EGCCFFfXjH0tGtNArnUSSV3w4BJKTU9WDJMAJ\n+Bb+119/Pb2Fb/Enj9h4cM2E5sFvFRmGStGy3oKFKvGYQeKtGZ4BYLqP/d1V6by/327MsViyhioU\nwx03Wzcduv3mWlloCrK0YmDrKthpta97W5gLay7/CJUe1BF+0Rx9KrSTdTqOY7XG/ofC/y++/BK+\n03kRnZJ8rhQYjAtrQ3fDjweyWWB7nIFOlinEn058Pvtx1otOJbjQgQvr6AOUURkRRPtrrrB0OmqX\nLdLyAxfC5Qn+gfe1N8eli2++Nx137tUu25lWWmbxtMun10uHn3YxJ2nynHDvvtjFoMD0wg8OCg2c\n8hks9VRcgrhtR3wirbjkIumY31xu2Cr/hXD1/x9m4Wqmnfi8mgAAAABJRU5ErkJggg==\n"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "15acbace-4f07-48bb-ae6c-3c60ec547335",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Sidebar\n",
+ "\n",
+ "Use the sidebar to navigate between pages. Check out announcements or documentation related to Jupyter-JSC in the links section!\n",
+ "\n",
+ "\n",
+ "### Account widget\n",
+ "\n",
+ "Use the account widget to check your account information or to logout.\n",
+ "\n",
+ "\n",
+ "\n",
+ "If you have multiple groups, you can switch between them here. If you are only a member of a single group, this part of the menu will be hidden for you.\n",
+ "\n",
+ "\n",
+ "### Main area\n",
+ "\n",
+ "In the main area, you can find an overview of your existing JupyterLab configurations. Using the buttons on the right side of each row, you can start, open (if already running) or stop a JupyterLab. Click on a row to expand it and see more details about your JupyterLab. You can even [edit existing configurations](#modify-an-existing-configuration) or [check the logs](#logs) of previous attempts!\n",
+ "\n",
+ "You can also start a new JupyterLab by clicking on the \"New\" button on the top part of the page."
+ ]
+ },
+ {
+ "attachments": {
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miQzB0a1bt+C0x3NJgARIgARIgARIgARIgARIwBcBODM++eQT/YoIDweNBEiABEiABEiA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nT56UWFA6OnfuHJY2WZcESIAESIAESIAESIAESIAEHAjAoJEvXz7zN3ToUPHbvXt3AA5Q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+ }
+ },
+ "cell_type": "markdown",
+ "id": "e2650da2-1edd-4e04-9d76-29d0c53224c4",
+ "metadata": {},
+ "source": [
+ "___\n",
+ "## Starting a new JupyterLab\n",
+ "\n",
+ "When you click on the \"New\" button to start a new JupyterLab, you will see a popup with multiple pages. Use the tabs on the left side to navigate between them.\n",
+ "\n",
+ "Service configuration:\n",
+ "\n",
+ "\n",
+ "\n",
+ "JupyterLab options:\n",
+ "\n",
+ "\n",
+ "\n",
+ "Use this to configure your new JupyterLab. Click the start button at the bottom of the popup to start your new JupyterLab."
+ ]
+ },
+ {
+ "attachments": {
+ "b6cdc836-381e-4c10-9860-22a8c8b65dbb.png": {
+ "image/png": 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TaH6DuTwE3pIA7yVvCZrTRGsB/d8RmRKi9S3m4hBAAAEEEEAAAQQQQAABBBBAAAEEEEAA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\nePmGte51VhIkTSQPnqhghed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AAQQQQAABBBBAAAEEEEAAAQQQQAAB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\n"
+ },
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/jjnsDLluucigfYlAoxdGjKE+pOrnYt/oA8rHYgUl1ikVuGsGBFjIGUQuUOAeDbBcnzyF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4LHgr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ffdCyYtdP/qa6/Z/x13nPdlZxhiXHrZZTZ3gSMZJk+e7H1psf76foTF/664wuYXFi5U1+jR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\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9L96/toxZh+QP34Mp9v/3/exsrf9L/maZTUGc5S8HkbL8BU2yI0PZ/v7pDWQ9lD/hTx5M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\nowyz+H0AAAAASUVORK5CYII=\n"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "850cfb95-7150-45e0-9ce9-7d115918ada2",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "___\n",
+ "## Starting or modifying existing JupyterLab configurations\n",
+ "\n",
+ "### Modify an existing configuration\n",
+ "\n",
+ "Click on a configuration in the overview table to expand one of your JupyterLab configurations. This gives you a more detailed view about your JupyterLab. \n",
+ "Similar to the popup when you want to create a new JupyterLab, you have multiple pages that you can navigate by clicking on the tabs on the left side.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "<div class=\"alert alert-info\">\n",
+ "<b>NEW!</b> You can change the configuration of existing JupyterLabs!\n",
+ "</div>\n",
+ "\n",
+ "Simply select or input the new option you want:\n",
+ "\n",
+ "\n",
+ "\n",
+ "If you want to **save** your changes, either click the \"Save\" button or the \"Start\" button. If you click the \"Start\" button, your changes will be applied automatically. \n",
+ "If you want to **discard** all changes, click on the \"Revert\" button to revert back to your previous configuration."
+ ]
+ },
+ {
+ "attachments": {
+ "1f3e063c-927c-4a22-b12f-ada9a910c682.png": {
+ "image/png": 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buWhAQV7u6tbWDSPGUB+26ufi2NYDlI/FCtpYphTgoREQYKbaVAVeqAmWYbIuuqtCKG3h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9rJNoCr5uuukmadKkCZ6p+ICjPjAud+3aBcsYj6klBfbzt956E5ZG3pblv6yQUlB6ade+rVx3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aZT2e18+jBBF7S/1bPLNd4n4aJf7KBYsH69dOzYEabZX1az30MffQTpHVDJkiUVFLtg9R7w\nY0cvnjOkaLGivo9ISdbsLGnevLlM/u13GfbYMDkPi8J0AR2znD17trRs0UJ+nTxZYc4//zyF4UX7\nGlwcjig4Ss+Q/XD++sJFPEc95CowJZ+tC1HFYZJeV1x82d9+521d5OoBs/90od7hroFKf5dXXvgH\nDRwosLggPBuei/h7cuEwgZ/paKK+dMlSwgXinC6UNOANdwfHHoU55F7/Z55+RuNoSlxhkFnO96/l\nRh5lSuH4DLgVK1ZK0aJF1M/LkkWLBRYmtP5cdOMvdlz4biudOotMhEl6FmVz9Kc5+EPqHYwF0JP1\n2IehjwyVAffeJ0UKF0KWGViInwNT+iOk/WkdpEzZsormr+rPgg0MNMZC5lvvkMZuIZKLjNjZjeMi\nLtXjP1jX9h1Olaymc+Wuu+6Wm2EqfQ+YdSdNqIRy9tlny8uvvCqdunSWYY88Er1zFiTQPdy1cKyw\nD2DezZu1wPUWVTB49513VEGidKlSCtqiFeNEsFtdPvzoY9ChPkzz78scsKD5nZqj//Cjj/DEl+QW\n8UlQtku63XDMEZcfiU6DcFef3rHeyTsiGMcfH9n+fDR0S3ZDeysGft+gsT/CZD8sHkhJHpviU/Do\njlLkg5DI54NbqKaO//hMxyMglq1YBuWB8i4H4kfaEnvtJbDqINNwtEHIav8DDvC8EUrn8nBPPky/\nuDJwpMQQmN6/SOofeqgqbpx2+mny/XeTpHCRInnWnznQhfYQ8OqxHC5K8ZfbrxyecMQFFvkn/zZZ\n/bAOo+V0eSAl/v/g0TOM9UWrWGE/94w4hcO9JEzzRxUEfXmshaOvoO5TFf6Cbt307gruMDAR21nB\n3aG0lcAMKwCAzZAhDw/Rn0OkQQo1c+ZMbX8vQYnp0h6X6vECXNjGDnu5Hcd+XNodx6K44jucLqkq\ndRyNOro4VyEqKfG3ctVqlX8V9iuPiiHO8zPfYQMcnzMd75CO/JdZKTNEa1jhwnsovaE17opKBHCh\nlgy/A0cfDBgwQJZzsR35V6lSGRCEc7B+nR50Rip9/yKVcExBcIQqXMS1UajAy59/TtcopdGQIQEs\nus8AjSqBj5kuMxPljWKQPR7S5B+ek/TXI3wAsV95966ZlOmbNG2i6WbNnJVWf6ZmwD333CPzoVA3\nEsd8vP/++4qxVcuWUDR4UipWrKDPhHPkIXUy5LURI6RXr2twVMpUPMelKFFiLw/PW0oqg+Z0SJ52\nd4zp8DNCc8XF1dHB8uqTacAm9UeopvPX5PvXBIn0m+B3OUYFM/wghdHf+A8NLtnmkn5rf6SNkzhB\nvpj8SdAk0f8G+gT+MfnrKBLo4J7APT5A+QgX6/9M/oQ2Qx5J+k3+JmSNa0A2/lMecb2QjX9jeerZ\nI2o/Qe6Gu4uP4ZWCuJj8TZc5Jn8CJwXeUE6JAt2TD7PxD5gnyTExL4XgcLf2RwqAbzxBlINwMfkT\n8wwpFHETPKSNo9kOWv8HfmCOXQa22rkC4uq323AikB+JnOzmjrCU7mh1E2SYT1SnT/C7CmmVQgTu\nREF2cLxB/tB9O7gHeI1kvOEHbeiM/sZ/bDNoWdb+KHlUZux68ocykXXfTvX3osXJGD7kdMRDh6sT\n2fCn9Ix6hoYg7lJ9GIs9U6dM053sGh4iCZhwa9eul9OwgEyFhF69e8ljUCYogJ33wZXyC7I8u135\nXSMydNcsn8uVKaNlmjljFs45b4JFwt/k1Vdfka5duob1KVcu4OfZ4zBRrwoJr2LhmDBaF19yrRsu\nro6hBMl7SipWqCDTp/+JXeehQikshGcpUGblytoHhhTPPfeCek/HWfMRcUJkdCc2l1du+LlgSIWE\nlligGjtmtLgFakAG9PD8/NNPWJT+XrpdeKHkx3nrwXF8Tqe3CN6Fxde4i8+Jn4uz3LXbqFEjOeig\ngzQnhcnx/kNYaa8EkJU1W7MP+H/n7uzqNTVs/IcfyjtYbI9dhhQo4N53vnwoCzJz+cUQfPe0aDED\ni4kBFyt1LHbTcxHnR5whH1KNGDUS/pRcAKsSAf/m65+Bhdm7HI1bgcZjPwCNnXIFS8Bz5elg2h7X\nmP9P4Q53PH87caKWad3addLh9NOhkPAKFgp7yuPDhmER39Xrr/D7N6T5NGzYAPlmyBdffiEjRr4u\nxyeUTA6GBQcuHn/55VfyAXbA8zx7pl23dq0c1fpomfjtt3IX+OXjjz+S5VDiuaS7U4YJ+MOOfSRy\nTiNyG38hGoTm+Cu2esEkcf3hlbJly8iCefO03IH+xEFFA76nvOivaJkdXOHCRVCnorrjXt8tLnpH\n3JJlS+UA7PgPbnfwiYvDNcokUSYAujqmpEvnzoqf7f2qq7lwO0Wu7nlNyMrdkce6dVSwcJnhmAvN\nP8MZCNtk/Ek44l+4cJGmKQ5rHqWhtFEeC9BUiJqd5X7k/zl4fvPNtxWPpgKKfJ7PNZAXhDkecfiZ\nt45/Pf69991HQb/77jvJQt5zZs/SO/1ZUK4qwoV2JTIw+MFyib331owfGTpUZs8CXCgT/LPnzBYc\nWaB5dujQAfGz5bPPPhccr6A77HtASeGzzz6N6K+A/kLllCWePo45YDFj5Ur54ovPYeVmvSpTLFqy\nJE3+sWg43kX2P6CW1pXvJl++fMls4U+XP6y/5q93kaGoxy233KKKJS+/+IJQqeLbb6G8BOJlZ1MZ\nLrx/+JDYvX+R/MCTxn/++4OU3gvKEnRDHx4K+szBb7ajK+T4HNCrebPmroooQ8GEPI1xIcK9sk3o\nvy+Urxi5HLybxL965WrlpyqwKpHuWNGUlul1KBnMR3t65ulnpU3bNjIGsuiCC7xCCqBcX8/UGfId\naND+1FOkAOo5DAppE2AhYy2U+w6FIg+O6yAQ6KM3KcA6+PK6EH/VMIefIfTxonf6N3GB1oCI8kuE\nAT7Qn0mT9Y/hc2ZKbC4zxYuL3nOC6XMCl+H3FErQhJR0DchRy9NIbxG9fLLoZvQ3/nPMYe0PjcLk\nj8nfSDbm9CRkbSRPE2EAN/kb997W/zv+sf6XDSNnWwrPNv4IxLH+Fzxh/a/1v0E0bHJP9LWRPEmE\nAd76X+t/A9vY+MNRYnuNP5LzTzty/RXnKoRXzK6Ty19wDMOP86AMoxkQJxpSshsAGE2LCZwfIbya\nYo3nvhGSdEypEEzkI7Acwnz8pJpmiBhCKgQRMG/N3/Ab/Y3/rP1RKOxi8sfLxG1t/xSmTvIG+UtK\naua8JlyAQ5DKXkaR7vzBtDkWZKiM8Of06XLRRRdhwZG7YH2sA1G45OWSSy7G7tCRcuddd8ldd94J\nAyhukSrgr1mzpub76quvak4M507iV//7qhzRuLHsgUXNdTCd3qp1a6HZ7bFjx8opp5wCqLj/Ieq1\n69diYR8wUzwMFnRiF+qlWDU44I9h6MuQpljUmzd/Hnayf+1ploHFR2dO/MgmR3pKEDalJuS5SLQ3\nF8HyqD8hGekwp+P/zzPPCs2r08Q7LSQU37OEh9MkEX7uWKc74ojDE2jS65+IUNj4EiQHQ9Lxz8FC\nJk2RN2/R3IOHcuIR9XHQccWOPLKxwo0aOQp3h/+HH3+Ecsof0grHEtDddMONQhP/03RHtwbBfP8Y\n9XBnfHCaty8OzrKSzlhkvvPOu+MSAu0r4AmCVIPZ9eAmYbGOJWrYsGE0/tASxsUMoHp/9tln5Nre\n10pb0hjvsTjMnNMF/BWghMIMv//eKT74IskHWDCk4/EAdN0vuURex7ESd5GP8WObDOOfzeHXxEDg\n8k3J7oUKyTHHHA2rDA9hEVKkVStHN8Ltli+/HAvaPQJLIsthhr4FlChYuJ9++ll58sqrrpRePXvi\niAy0C5hx/+STjzV7Ho3COujwyleA5aNbj3bBSA2Oxl8OVnf4K1S4eDgmxe8wKKssW75Cxo0bHwBk\n9HvvyQrwTHL8p5j0EoGleerWqStvgPY0nR/cLziO4jccVeHM+7vQFAZ8vviuPhqcnjGf+Eviv7Vf\nX6lRs4YuQr+Oneh0ehQJ7jNnzsDV5UuLGOpQceIJ408e0eAjZNXq1SjrG0pjwtSrfyjymCULYEK/\nDBSkyuK3Cov1TZs1lUdgxYMwOrrVQvls0m6KydeLfjiPv/b+WMyH+xSKAsy7dJmysheUDmj14YIL\nL9Dxr1qMQvnX+YXoA/bfX+vPMlNppLQv0z333gMLCcfi6I8F8sILLwj5mlZAGjU6TPr27QeeelSZ\n/icoONFpSXxx+Fy3bl0ZgyMOFuG4BnWoz/+x9x2AWtVI23Pp0i10kKICNlCsFBWwd7ArigqK2HtH\nBV3L2l0VG3ZX7Lj2su7qisraUBTERkcsFBXp3Pv+zzOTOeXl4mL5uPibKCdtkkmeMzPJfZOT3KEb\nljrLp3hPHTp2gB18Rq8dMYKCfPbZp/j3mWy26Sb6UrLyZzR85u2PySVOHVkCmQV/vQ4DvHiyw377\nHyBNmzXTDTssuYRyrb0luHRWl0INZjn9C39/UKbbAyO6f+F6lMaNG+FfE8XpKmzo4YkXM2d+Z39/\nZPqvBYKcaBhMNLuEFaf4r9OqtbaI41WWP09fYYEOurnLavNn6dIynKSwrRx55BGyFjZ/HHpYHz3t\nhe//zTffNHaojPyWlpZq/HVsPKK76eaheq3PpjhB5kdsAHkXm6h4lYhiEWBRL4RZxnWfV82Yc71K\nO6yhNFpEx0pC/7W3IduSQ6QI/wz/lNpDkb9BnQIe8YdspHAEQXE5QTTKX4Anr1hpLOpf1v4WmSk3\nPEVylQpc1D9Ak8JRhBOiUf+i/qlUpBaH0TQW7U+0P2ZCVCZSwVCpyT98XE8NTrS/QCiFI8DlOCEK\nPC07D2wai/oX9S/qH3VEdSJVjKBLWc/1KlU4DaXRQOx0Vqll5ytOY1H/ov79/6V/kGgbcyjk+FdR\n6+9VvBVUQPzMrs3iD3saV61Mj49nK9lQngPMH76QgyNb4Wvrldh6w4tRVWJJjB+AUaF/6cWfg/VH\nM/yoWNAf/MiMdRGSyJ9IRPyj/EX9gwn5s9sfWE9qgl/foHbyF9tfrQSWlc7ssVl6gEsbHZzKW6Dw\nNMvFaQVYKDrqqH5YVOmHe7RxjL6+F1KZzXb61LfaRuHO9TvvvAt3dteRhQsW6uKYl6G9P+mkU/AF\naX056cSTZdCg85E/RBeNbrrxRvkWX5TaZoCC3PC3G3UBrMPGHeQ13MP9GhZJffzhXdqnnHKq3HjD\njfI5Fsg6YAH0tddeldf+8xpYWX/r1q0rp556Kppn8Z/rf9/DDpOTcJQ/r5u45ZZb8NX0TDn9jDNl\n7733wrUQXCSGQ/cW4yh/boDo0aO7JuUfjmaaalim/Pml7bFY6KZr02YduZwbNgI5/b179dKFQjL7\nZPx4zWnX1k4jIH97dz+Pf6hOYeDIWtz/z774Qkk2WH9DqzJQeLkXnn9eLsaC7+Ahl8jOO+2ABbcO\n0hML5aefcYY0bdpUmjRtLCeffLIe538q3gHdAJzmwAW1I/sdiQX0s/VrZy6mk/fJJ58C367h2Aeb\nRvbeu7ecc87Zek3D+htsgOPob5HWOLq9I04M4P3pPCHi6KOPwkLt6lqOdYz7ZByOxG+EO9R5/cKy\n/d9vv/3Bc6k88cQIPX7/uHBc/TrA+Ior/pqUofz16rUPNnp0Efb/DPSJ1zRsvPFG8jhOQ3j66af1\n9AQerf6WyvGd+rX4AixaDxk8JJE/yuGJkF/K8WU4hv7pp5/BguPjuhAKZomzd8sn7rHHRoQXX3wR\n4RLlTyKXmJ1x9cVjWKCl67J1Z83htQY8beAhnMzRo3sPqVG9htyORebXcZUDVZjH/NMpD9VpnG5S\ntaqmsD3tsEh7xOFHapz9pvMZl0bwcP6sw+wu3iWuFbj0L5foVRpDBl+IL+bL5JJLLwEtpclWgU2u\nvBb3UZs2xuLnQbd323V34N1bLsBX+1xU5XUZtevUxSkqByiRtgptzxQLYZ8ZhhaCoJj/arhi4AF8\nfb7lVlvKYYf1lfHjx4UTGErkggsu0NMn5uMKgjPOON36iTq0/4oVLtO4aLAuFlPu9Jj9b76Ve3Cs\nPtty7rnnyPPPvyD77tsb5c/ElSGN5ZIhf4Gt+UIOOuhApWF7tGLvPiMsbA9N1Sji2htEGOoPfC8G\nvtSTb8Cza5euct9998rI/7wuDw4fDoSBcjh94WmcEMINXEdgYbsH7NCjjzyGfvWXgw8+GCdrvCvX\n4ooTbhTjtQI9e/bAqTHTED9WzsTpEdWqV5MbYUPpuoaNReRvNkSTVad5LcpBOA3kgkGD5Isvv5TL\nL70MOrAHvs7fXM4//zyc6rG77N27l1w46ELdKHbCiccLT5PY/4ADtRLrMp/hXVnVOftTE9e+EPZr\nrr5K9tvvAD0hhnyvwvUN+/TeR8aO/ViOGTBAS/I0ENbF2tSFAP/eWEb+nAiVcyMG7dQjjzyqenMQ\nMHqHGF17HTA5RpphPJs37yfUqy1OqmYsTbGwn/KSxf/ss8/FxqQrMKacAhk4CJs/PpWTTzpZr2Lp\n3LkLasn3v3KVyrJNt23k8ssvw+aZdrItwh98+IGMxaaRQw45WPnz2hFyvAd2rzL6wI1HdLfeegvs\n35oya+a3Muj8C7RR339PXNhS9EAbnG212NU6SLr66muA8b46juyvdrEUdtE2//31isuxyeppPXWI\ntnwGrsUh/nzf5513HlkHLIr0jxnavcA/hxgz6fL9Z4q10MowVix/XsLoWMLLRP5m7xwhABPxh3BE\n+VP9j/qntiL/yOhKyIj2h0BE++tjUxx/oCM2eVANcY2J428wGEFbKC9x/A3zNR9r4vwjzj/i/AsW\nwixDajE85NbU4zbyxvE3zj/i/MN0I86/gMMqNv+itaKGVuz6M1vRfWBh4qTJhYWLFxcWLsK/hUvM\nX7zIfKQtCv80nzTh34JMeOGiRYUFWkcovzClW4S8hYwvXlJYoPU7D9IEPpG/4RrxNxyi/CV6FvXP\nbJDbHff/f7c/E6Z8pTLwW94/vkLHR8Vl+j8ChTIE864oIYkmgTx5JpanKCOX1CFy4YUXcgaiW9bo\nYxDmSBz+ScHbhoXewsBjByot6dq2bVu47fZbk7qwQJPkaT02s9M03FWvdF27dgk0uqSao2/RvNkv\n6v/IkSMLDRs1SOrYd999CzhWPGkPA+PHj9f8a665JqQv2/+0AMAI4Dj+uFecf9EkPIr7df8D9yXF\nsUFC6fDla5LGQA5vxHLxECG2mGgULhh0gZZ1/ozg6gat9+2339Y8I/BgWeH+++/X/AcffNATC7iP\nvYAj85N2891gowjyU/7XXnttks9+deywcWHMh2OserRr4sRJmj9gwAAtxwycglHo2bNHWg7YnH76\n6QUcIa/l/FG7Tp3C7rvvrtFcfwN/nGxQUJlApmIM/uy/4lskf+wf2z1x4sQCTi9IeYP+8CP6FmbP\nnq18XI6L35HHWZ719OvfH3WUFCZPnlz0ctAY/E9XBv+/o95WXjvuuCNTNN0fWOy2vJ128CT1hw8f\nXthggw2SNnbv3r1w5113afy2224DTVlhO6RtscWWVg7V4hQOzWf/cdd9oXuPHoXNN7d8fEWu8vfy\nyy/l+NSpXaeQfS9ffvllAVdbFHCtRKFNmzYFLB5rnaTJtzx9/7g6oNCmdZtMvWWF++69r1Cnbl2U\nNf3fYovNC6NGjdJ2kxBXiECuumXgyNfulR162GHKn3PKPEVZ4YKLaG9KCjipQ+vBlSBK6+//osGD\nC+uvv2HhtNNO1+r++co/Nf/EE09U39/nsGHDkJ/WjqtnCrhmJqGh/D/22GMJBTY+aN78hQu8mYV/\nvQJ8gTtlUOsK1dWuXRf4HpPQffDB6EKnTp2Sutuu17ZwySWX5Pj7e2R906ZPL2DDVgGbOZIybBuu\nASjMmj0rqXf4g6m8sP8NGzQsPDFixM/a/4ceGl7g+1ccoH9sF05CSOq87777CtQ/xwmnxIR3SJKy\nQrdtt8F77Gz0ob9Dhw5V/ZuMvzHoPoN8N2zYSOs47vjjCnPmzM71hfxpU3feZSeVudLS0sLFF1+s\n9DjVResg/pSX9A0VCkOH3qw0uHpHab79JsXI+48NU4VZs+ZoPu0K7e+QIYM1njySSpNAokeOP21q\n9p0wHYv5hW++/SapJtc4RGbPml04pE8ftDEd/3BCDtozU8ssgOxQ/lkX9Yx4nnvuuTm5O/roowtn\nn3OO0owdO7bwE/sAemzmU3pv8eeff67vm3nHHXec5jVv1rzQvEWL5P1TXtj/iRMnKP/Jk80m9+/X\nX+NZ+Q8Jy3jOzzJS/dd4LhOREKf9y7uihCSaBPLkmVieIvLP4VEcCfGIf0aANJgDKpHTTKC4QBLP\nl4zyl8OjOBLiUf4S8QmBHFAZsStKLy6GeJ4iyl8Oj+JIiEf5KxakHFAZoSpKLy4W5S/qX04mov3J\naUxxJMSj/ckJzTJWJFWqHIDFhTSep4jyl8OjOBLiUf6KRSkHVBz/EjiSQDFgSTxPEfUvh0dxJMRX\nZf2bOHFiha//T5w4Gb8Xdj+2MPGec/QoWPxght+x6PBrFU47sK+RyvCrPnZK8qdF/rYfPKVCVJND\nomfxl7K0phLUhGPXkcbbGirhNzn9NQwFySV1VuqG2+6UKVN5Z7XFSaMhnKpQFV8c7bZTD9mm89Za\njD/v/d78rT0rr/9f4c7bl/79HznikP3BukQGXXolvvQsyKUXnIWYobYy8V/Z/Td+fNr7jvyJwMqT\nP8Obz4h/1mpVlP2z92Hvf8Y3s6RpozV/k/2dgXvKW7ZqBYnyN2zvOfu2jWc+xb8PYCnaa7fnRhue\n+SJJVnGyx83PPpMiCBSEXzPzhIRWaG/KNQ1lqTXsFRdlFCd7PMvZ09Ki+RRynTZ1Or6Arye1atdZ\nKf1P5S+LesX13956+fyxYC9LcVpEQxyPXuyIJK8UmDJlit6lXh/XW6yo/M3CMflz5nwvLdZuLtWr\nVUdN5fNXnvlXljSjONnj5mefSRHlMxdXFXyNL4axsUFqrFY9SP3K478i738qriOoj1MisEmA0GSL\nJJ3JJs/9ca7Ov+rhtJBsz7M0VjCfwl7z6/LJkyfi6/jeUq0KT14QwcKrNG7UGCebDJJLLr7kf/Jn\nGa8Z83KZgS/4qU/16tcv0imnYonsW/9t+JcuLZVJUybL2s2bS5Vq1cDT+OS58eSTRXpNQ8uWLaVy\n5cqgcquXhmbMmIHrN6rjupY1vJG/qP9ZzsX8f/jhe1xv8RNkr3m5/ed7JCo89cXdIpw+M33GV7CX\nraVSpdRCZ+ueOnUqrgWpLM3wNfzP8fe3hL84Zfr06cKv+5vg2oPsmyBfooENLvgav7bKYdFL1KZl\n+VsZg8n5l5aV4QSaOcCxvlSpUkXLLFgwH7b/O712ohKu+ElRT0NKmH0UMwp52eRFC/FeMQa2wZgC\nkJb7/r3/Xn3K1ULl4V+KKygmT52Ckxdgq3Aahbssf6Z5nP6ihfNl8pSp0rJla5x4kpZxKtq/WrVq\nQ84oq/jbqaxUJk6eLC2atZBq1UwHnU9ScZJgAfIpwzUQtNGU1Up4lytqf72q4v6n0uUU8L1jmSQG\ni5M9bn72mS3oVJYW+S9rf7JoaTgPWZJdnOxx87PPpAgCTmVpEf+I/8/N/1RK8iKTCFNxssfNzz6T\nIgg4laVF+YvyF+XPRm2XhKy2aDivMkl2cbLHzc8+kyIIOJWlRf1z1FMksmhpOA9Zkl2c7HHzs8+k\nCAJOZWkp1zSUpdZwvkiSXZzscfOzz6QIAk5laSnXNJSl1nC+SJJdnOxx87PPpAgCTmVpKdc0lKXW\ncL5Ikl2c7HHzs8+kCAJOZWkp1zSUpdZwvkiSXZzscfOzz6QIAk5laSnXNJSl1nC+SJJdnOxx87PP\npAgCTmVpKdc0lKXWcL5Ikl2c7HHzs8+kCAJOZWkp1zSUpdZwvkiSXZzscfOzz6QIAk5laSnXNJSl\n1nC+SJJdnOxx87PPpAgCTmVpKdc0lKXWcL5Ikl2c7HHzs8+kCAJOZWkp1zSUpdZwvkiSXZzscfOz\nz6QIAk5laSnXNJSl1nC+SJJdnOxx87PPpAgCTmVpKdc0lKXWcL5Ikl2c7HHzs8+kCAJOZWkp1zSU\npdZwvkiSXZzscfOzz6QIAk5laSnXNJSl1nC+SJJdnOxx87PPpAgCTmVpKdc0lKXWcL5Ikl2c7HHz\ns8+kCAJOZWkp1zSUpdZwvkiSXZzscfOzz6QIAk5laSnXNJSl1nC+SJJdnOxx87PPpAgCBVy5PBmn\nHzdDuOLWH/kbq/4iiN8h4fhDIDci4D+u9usVDQYIV/65tYA0WC/Xb32kEvPo8GQFJegIEzCP9j8m\nmKdZ+MGUP3bqgb+2iwA0VlprYP0sz5/PSzEZBP91W7eW2nqkaQH36ZbK1KnTcM/yPHny2ZfxA3l1\n2WrLTUMNvy9/tn1l9v+Wu+4TfAwGGAz/ejjSuAw/2nITCNvBqfHKxH9l97/4/Uf+K1f+Iv40X8Tc\n7M8qJX98Ob9R/2kkfUHB/OxTGZiN1v77H+IoQzutpEZPW84EfVow5COicdCFdC+hCYqt8bH07DPP\nf7Waq0mrVq008ffmz/u1yZlNLHb2/pEKe1ulsi2SkX+LFr5AyBLWOX2Gfho+iGgctYd0LoRahHlI\n54Ihxr8sfy58QuhY8SqDv4021qb/hf8aa4SFWXa6qP+sgYuNbXAVg/ZfexnExUALT8vQOUKQvzXX\nXFP4T+vUbGtPYGKsAs7Lw99LaJlfIH+1cUXCenXWC42CpxV5bcZUn/9H/FcEf26Y4PRMnTYNEW0P\nIqFd3mIm6OaFkGBe9mnVZPGn/aPj+//8s08FJxMIThiQ43Ds/ezZ38vZuM6AwOy8y84Bn5/nr3OX\nwJ9XfTVD+4vd8vj/HvhTz9Zp0ybDsvz+V8MGGF4dku1/Mf8mTZooxI6z5a94/7OcAyTaLvaf15HU\nxVU07or1r27d2sjK29/qq9UIOhZKlfP+WxDvwOzn+Nv4x7l3iW2MYF3qvKWhclSGr+6TOk3HQEhb\nVg7/7Pt3/pUrVZaGDdYK9aMYyvHqlLVbro3aAxXtpgYt7pUri7QpqAMRjYMupHsJJlSvXn2F3n/S\n/+XwLw//yrhaw2xc6Eo5/Iv7X6NGTUmu4fFiKOf81fYxPdRVCVjh1BG0igkhOeRZUxHReL7/lPsG\nxJg4Bmeh7NMy/i/1r7j/Ljhpq9BEtN/7/3u//8gfiAaws28+4u9aEeUv6l+0P9H+coSt2L9/I/+I\nf5z/hFnK7zT/j/O/OP+L81+b68b5P3HIomC4xPlvnP/G+a/Nf1VD+ANzBa3/k7+uwKjRRkMqYSLA\n37fszlabIiOmP4gx3cLwQEsa7EuQAhdcTM+VgjXY9gWrR/MKllLQzlIBvG4tonHSGX+re+9ddsCu\njabK0+QEHFMAAEAASURBVKgK8vSLr8hrr78lb733nmy5xab/R/yt3f4nivWZ7aUjFnC/Y//59SDr\nZZ3s/5knDSQDRB2jlY3/yu2/wkm5SN5/5F+x+hfxX3Xwp3ZQNX6j/VHrFYw0zQ31zSwZq8/8aB1o\nNDV9ZFM1TPunNioUdgL1Qx6LJ39YZvhVEP/NNttcxnz0IdpkPWczcg5t73voYXLvffflkhnx7iXh\nn+n/nNmzZI0119Iy5fFw/sPuvFP69eunvNI/mrKcNMvy06C15Wf4s4P6blhmFcI/QbGC3n/kH2Tr\nF+C/3/4HyFNPPy1DsCmB/9zdeecw3HnfTXVJjYeLrfpR/qL+hZ9Wo/0JZkeVztTnF+if65urF+Ma\njvbfxjjFEog4QOpH+xPtT7Q/pgquG9H+JEYi2l9AkZEHjinBfgat8WEn8UO2xjUcx584/mDQNTFy\nG0NBoojE8TeOv3H8NVVw3cjY2zj+xPEnjr8+WHDAiPMPNRYcPkNAUUkf2VQNx/lXnH/9H86/KnL9\ni1JfRRcuENBFcFyRYCcaYGKJYw04veRCefiUBaSciDI1GBJqCPcbQEmoUAz6hMx+jwwTVJTjFQ60\nxUyn+um/kKBbIVgv+Fsuw0hFfpZ/p44b6aaEeXPnmSFDNWU4ZeD+hx+TTz77XJYsKZWq1arIVp02\nkd577gouxv/+hx6Xj8Z/KmVLy/Qo0xZNm8jRh/eRGjgilQ2a+9NPcsc9D8jXM2eivjKpWaOG9Npz\nF9m048bK/+33RsuIZ1+QTTfZWN5+ZzS+Aq2MsjUEF7TKRWefJmy29//iK6+XalWryjmnnSAzUd89\nDz0B/ztZgq9nK4FwrbXWlKP7HoxjVVeXy6+/Ece5LkaXC3LekMvl8EMOxEkQL+C0iDI55/QTtPWL\nFyyWe4c/Il9OniJLcSQr696sw0ayX+89FMJ33/9IHn/6Gdl1hx7yymtvyE/z5qN9lWSDdu3k8IP2\nQd04svYX4F/R7z/yh8zzzVeQ/kX8VyH8Yd90osKNS2oPf7n9VYOqllDNBc2d1WkZmadadUie0YEo\nDWsiEjJOY3xoXlo2VzvtOytaBfiP+McIWYyjvL1F2q7s+IOc2vhSOclHs5OwBrTHCQIa40PzjJLP\nuvVXl08++SRUb+MPXp0Pk0mdjfVo9KQ6ryjJ//8Nf0MIcAELlels1zWcYujYehkDRVOTUk5TjH+u\n9lVI/rwvf6T+V8Px8g8//LDcc9fd8taot6QmTpLYtENHvcJAXwRfgnYsfXcR/4x0R/mDgFBIdIav\noT+S/LvORvvDVxjtL1BIXBx/AEW0/3H8i+M/9CDOf3yMp5+MFHH+E4xknP/4XCrO/3IakswnwkBi\nU02mQokcszj/JB6JVVHMNMZHHH+ATRx/qDBEgX4iKXH8UTwcG1UXPDIIKWL2SGVI8cPDUpCrAU1N\n6J0m6h8gifpHEKL+qXZktOuPZn+Czlfo+iMwrEKFos3hUhzvZuDYzzgftlEB4ZCmcTdQ8JUuWCc9\nOUHplEDrsk0GqB9JdhytXQ8RSupKTQmOW9ArIZy/DilaqZQgg5NYJi1cvFgeeHgEihak06YdtQpy\nuumOu2TK1K9w7HZlabduG5k2/St5Y9S7sgD37R6y/z7y0r9ekw8/Hic1cTxsy3WayfSvvtY7XW/D\nJoSTB/QD71K57NobcafwEqlVsybubm0iX0yYKH9/ZITwmNmOG22Ahf55sgj8//v2+7hvuBI2GCyV\ntRusCbpJ8v6Yj2SzTTqgWSUy/vMv5Efc/du+LY6Axg6N6265SxYtWiQN1lpD6tetJ1Nwn/J3382U\nux94SE4/caC0wtG6s2fPQdlK0gJ3CdfFdRW8V5jXNzj+191yh8zEPbN1sCDQolkz+RJte+vd9+X7\nuXPlqMMOlvkL5smSxUvlKVxrUaVqZdyd3ES+mvGNjPn4E3n5tTdlx+7dfhn++g4r8P1H/hWrfxH/\nVQd/2j81BGYxf539hS7D2fiYbBljSqjUPYtn//YjPx0QUtJAjAR3Gly2rIKYqayi+bfBHd4BBLQ8\njFF5z5K9X/B/Tf8r497y9u3bJyyS6pRXYFgu/2Ux/DX88/z05WhSReMf+atAQev4TvKCZ7FV9/2v\nVqum9Nh+e225Nj0RMvaJkWXbbl3UTKWO758w/Xntb3z/8f1H+Y/6/0cc/8Lwlo5/mXltHP84vMfx\nn/OdrFjE+Q/FIs7/qB10cf5jIET7T534Y/39F8e/5I2FQGrX4viv1k1xyZj7ZXCK9i/av/j3X/z7\nL47/q/D4j2Vnb53+PYP1H19/1xkL4xoAFXwGffz7/db/9aSEUC+vWMCokjSKIeNtf1toY4yWqsUm\nkTosm4EmhDgygdY8bXaoB/S2TTaUMyPNBXndYxP4W68LcsNtd+FkAeTwX1mpLMUpCCW4KmI13KPb\nY5suhAObEabrvxpI+8v5ZyGlIKVYyBv0lytl9JiPZZ89d9MTFNiYEwf2kwa4B5utvPTqG2Qp7/1G\nm5967p9Y1F8i67VuKQP790VbRL6dOUuuuH6oPPnMC9IBmxI0EV2pU6eWDD7nNN1EMX3GDLl+6DB5\n47/vSCdsSmD/Xx35BpslO/bYViZNmYoNCQulaZPGctoJA7RbpejDORdfLjPnzFG6g/fbSz7+5FOE\nC3LskYcG0IgZ/5XIh2PHyXezZuumhrNPOUExLV2yVM5F/z759AuZM+d7rYd94p23F519qtbx6WcT\n5I57H5Bx48frpgS2nzQrgr+9WVaLkJbTKr1JTGJN4fn7v3+rOfKP+EMS8L9qgguFJUX5+6X6R20l\ndsH+KrCqYmZnCK/tXNIQY+ZoexHKpeYiRpa8oyx1EV3kH/GP8gelUL0IymF/qVuSbwAKqqde1L+s\nRTFkiuwKE6P9gVgpLhlrXYRTtL/R/kb7C6VQvQjKEe0vrKf9zR/nf7CdZkRtnOEzjr9x/FUNSUXC\n7EcmjmCcf7jqxPkH7am64LmkxPkXkIm/PwTxcBkxfdFY/PvPjYirjMYzFqVcvWJitL8OXQataH9S\nOaKMAI9ofwgCYQnCEf/+USwUjWh/3YhQQMzFv3/+ZH//mGXgKKIhKEYytoahhbryf7n+S4ZVlD8f\nWPBngjKkguIEA1pyXZbCfoWQTRI4PpljGxn0igVNq4QFe9bAJXAlMVJEkr/3yQbFCqi/EjYkBMqE\nATchsDCvSKiCkwoWLl5kGxKQ3nmrzWTfPXZjzXAlMgbHZHOw4SkEr73+pqbxmojaNVeT2d8vlglT\npkgzXNUwddp0ufrGW2Xd1q20jkFnnKL8S8B/wsSJ2tZ6q9eTV19/ixWDf0Eqgz+vQkD1dqID+Ldt\nu47SsonNmzaVmjVr4PSDr7S9TJs0eape69CyRTOWkiv/coFeGTFz5mz5ctIU+XLiJEMNV07Y1RS2\nIYOYJQCjIuKDEVTGhg0LO2y3Dc+RUN6Vq1SR5k0a6ekQvLLC5vklsmH7tqjb8F9v3dZKuxCbLdT9\nEvzZ44p8/5F/xD/Kn9lf6ILbn19tf80CWD1amRkS2p9Qu1HQgLmDvbACwcvaD6dR3wjTkrT8SdEc\npdI4IYgi/4h/lD9XCCpNJpxRIk1FPJudKpYRpiWj/mWgS2EivIw5UCCK9ifan2h/XCGoG5lwRok0\nFfFsNlXJnBGmJaP9yUDnIKmvGDlQIIr2J9qfaH9cIaAiWQOTUSKlQDybnSqWEaa1RPuTgS6FifAy\n5kCBKNqfaH+i/XGFoG5kwhkl0lTEs9lUJXNGmJaM9icDnYOkvmLkQIEo2p9of6L9cYWAimQNTEaJ\nlALxbHaqWEaY1hLtTwa6FCbCy5gDBaJof6L9ifbHFYK6YWFKRUWu/7Iden0DWqG7yHifvCor20dD\niNVxvYIBC1hU9mTFiXmI8MgGT2Z+CTYp6DJ7KB/2LGgfrUI8daMDxAEFyrgwphWTF2rkYiQdyh/f\n/3Bp1riR8n/o8X/Iu6M/lPdHj5E9dt5Bqletpvy/+3aWlpvxzbfy1IuvsHL0CfVopSXy9YxvZd89\nd5UZuM5g0tSp8ukXX+CKhS+x2aGS7LXbTtKl8xby408/aV3vvT9Gy3NThJl2tAPNmfvTfLTH+tlo\nrbW0bUwnTaeNN5aROClh1DvvS3Xcv8zTF7botElC89yL/5LX3xyFdG7eYNus/9xgwP6y/4SQzfX+\nE2llh7Tvv/8BfEqkcaOGZKntQVQ2bNdOT4j47rvZsvoa9TWvfn36AX+sYJaUYMNHKa/L0G5Z4cCf\ndZJnefhX9PuP/NP3XxH6F/FflfCn1puByOo/FZ72Z0XsLyoIdkNrQClqP8ublWM4cSBRe+SJtMc0\nRh5PCENAiS3M2lmj1e75mhj5Kzh8EIqIP8Upyl/UP7MZqhb2QEK0P2ZyFZBofw2MOP5klCQTjOMv\nBxJTFTzj/MNnF0FG3MCqz0ecfzhCcf4R5x+uHkFb9O+UOP+I84/wcx/kARIS//5NxthETzwQ5x8J\nNmZLfHQJALmBUZ+POP46QnH8jeOvq0fQljj+ApA4/4jzjzj/CBYhzr9W/vyTKxT8TQW2qELWH8EY\nJyWgBTyxgKvUcP77n8bS1gVrCQL9it+iWkAfnGqwrA202iHE9dSDENE8WFylAC/lE+qiJebJCamz\nerz8QfvtKVNw2sE3M7+Tm267V04/4Wg13rVq12TtslnHjaTntl21zmwtXKjnFRAnHnOkLJi/QN54\n+z15f8xH8s23M2XEM8+jXAdsJqghc+fOl7599pNGa66ZNIH1sI21a62GgNVauWrlXP+377GNvI5N\nCW+98y5OVsD+DvDasfs26LjIhx+Pk3/j9IYqVSrJ5p06yPpt18VpBu3kwsuvktKwWUCxNERS/FGW\n6JBjbVzJQP/Hn+bi2cQaBMwW4PQI0jRt1kjmL1ik1JXBuxh/ky4URUdWFH99LyhCvhnp5MtM+LM+\nRlP3+71/rzbyj/j/2eXPdAyaQPvzq/WPckRNNc3KBM2sMYHKRh//NN+T1P4jkhDkgwkxkq2cl2YZ\nOI+qn6EI6WpWGY78DStgEaAxSCL+KkapgGRkhTkOVhLMJGTzNdnyMsEo/6537gMcQykgHuWPUgTn\nAOWDCVhIzkgXC5hzMNXPUIT0aP8AE7FweBEO0ET7R2ii/pkeJQKSkRXmuLAkwUxCNl+TLS8TjPbf\n9c59gGMoRfuvkET9oxbBuYDkg4mwIDmjXSxgzoVJ/QxFSI/jH2AiFg4vwgGaOP4Rmqh/pkeJgGRk\nhTkuLEkwk5DN12TLywTj+Od65z7AMZTi+KeQRP2jFsG5gOSDibAgOaNdLGDOhUn9DEVIj+MfYCIW\nDi/CAZo4/hGaqH+mR4mAZGSFOS4sSTCTkM3XZMvLBOP453rnPsAxlOL4p5D8yfWvotbf+NE+JBGL\nynwLDOoTVyuoHx5YENP8sDCmNIGei2W2hG6CrPZDayEBv/u39DRmdZaxEteAIv4sVcz/mMP7IK2S\nfPX113oyAStu2aK5tmvCpMl6mkAjnCjQuGFDGf74U3L9rcNk5qxZcu3QYXL24EulpHIl2b57Nznr\npGNx/UFjNLtEJk+bJg3XwkYEtGPs2E+0jiaoY3Vc5XDzHffIrXffb40NnfA7ibz/dWrXkgZrrCFf\n46SGadNnyOr160q9unW1Yx9+NBZ+QXbZoacctO/essnGG8r3P/4oixcvVUC8/9w0wSsnHH82xvvf\nspldA/HmqHdz+I/5aJzW3X699YrKabLyd9R/Df4sW4z/ynz/kX/EP8of1Njdb7C/rII2gM79JJzY\nX83OPGyTUZoAQi/sZTxT0z0TiUkwCSRJaUog87rc9zpRIp+EmBfOZ4R0z4z8E5zSQBLKoGRpjqX7\nEf+AQJS/vEgg5sKTz4j6p7g4ONH+JHKSBpJQBiVLc1lyP9qfaH8UgWh/8yqBmCtPPiPa32h/s4NO\nKieJwKRJLkJUMQ27LLmvume5+STEvHA+I6R7pldsdXh1nut+kut1ue8FwCyfhJgXzmdE/oqLgwMA\nk2ASSJLSlEDmWLof8Q8IRPnLiwRiLjz5jKh/iouDE/UvkZM0kIQyKFmay5L70f5E+6MIRPubVwnE\nXHnyGdH+RvubHXRSOUkEJk1yEaKKadhlyX3VPcvNJyHmhfMZId0zvWKrw6vzXPeTXK/LfS8AZvkk\nxLxwPiPyV1wcHACYBJNAkpSmBDLH0n0Ut2DFrj/iiAQeA55IA470Z7Nsm4GFGMY/rBLaFgPrkBfh\nPgW94sGr0F6RkpsVGEkFrFJYedd6vQLWmoRZF7kvy7/e6nWl53bdlMuIZ5+XhYsXy1abbSo1ataQ\n73+YK1dcf7O8+vpbcvu9D8i0r7hBoL60aNpUOmzYTq9PuG7oHfL6G2/JiGdfEF73wEXP9dZtI733\n2FlbOXrMOLnlrvvlX6C55sbbZcHCRdhIsBFOWgBEpMBJDuX1v8vWm0tZGS5OKCuVrbfYLLzVgmy8\n0fraj9ffGCWjcTrDK/95Q64bepv2jNdU+CaEytgssWTRYrn/kSdwgsN3uf5v23VrqV6tqnzy2Zdy\n/6OPyzujR8vNw+6VWXPmSANcJcFNEcTLmAYQNW4t/TX4V/T7j/z5ApeV/5WlfxH/VQl/s6G0P7/H\n+zepor0IZkpDRfa3KJdRtSyAJWOmk3Bqf7RgpmI1RFraQ5F/iqBjQlRz45/CmOYyGvEHCIAkRS8N\nR/mjhGTkJQl6IJ1/Rf1LJcjRifoX7U+0v7Qh5kxDUu1gahx/AAIgSa1HGo7jDyUkIy9J0ANx/HEk\n4vibapBjEsffOP7G8Zc21Fwcf4lDah0Yi/MPgyS1nnH+4VjE+Rc1JKMvSdADcf7lSMT5l2tNVmLi\n/CPOP2hDzJmEuMZk0pCUSk8ajvaXGGXwSoIe+GPZ31Vh/a+SYNG9kgpcEEcssLvIUeAKJWU4xQHA\n4tqA7CI3y9DR0JeoVvtLcJ9lWRfimgRKVJgINgKaW8xfiVEnNkcU8991hx5Sr04tKV1aJsMfG6H8\nTzr6SKlfr458991sefrFf8rnX06SRg3WlKP7HqL8t99uG2nVsrnMnvW9PPX8yzLyrXekUqVKcmSf\ng5THmjjp4LCD9hVezfDFhIny7HP/lB9wosF667SSXtiwwP6DXK9tAFgImJB5/7tutYWeclGCbQ7b\nYRNB6Kxsig0Na4Pv3J/myYOPPinPv/gvtL2urN2iBXAoyLjxn2v/t9y8k7Zj9JiPZdR7o8HBeLBz\nJWBywoDDpQ76/MGHY+Xhx56WSZOn4KSHJnLmiceQBFyJIvFiI+kUbHtqIxHXpBXEv6Lff+SPd2ha\nUiz/Lht8nS5/1KjfVf8i/qsQ/pgw/h72Fz0yt6z9pXHQfWhB5myzlMmflTF7x2yzLJZnYU/z+svz\nnZJ5kX+CLAKOZMSfshHQUM/CTNWxzbwofwEPeq5V7mtWuY8sRdS/RLIQsHC0f9H+UHGCNKhnYVOn\nOP6pBQEkZkkCTsHWZK1LSCryshTR/iSShYAjGfWPIhPQUM/CJkhR/6L+mXhE+5PRE1OOZB4YouV4\n0f6moMTxJ7GsCFg4zn/j+EsNCdKgnoVNb+L4G8dfE484/mb0xJQjjr8Bh+V7cf6RYhPnH8nIgoCF\n4/xjlZp/rALrfyWy3XGFifeeLU2w0E2nAGE1VK8USLVJv+wvw0qoqhUFCjQOJqctlbB4xjR1lDau\nmrKDWOViftY0MY1XIbhQuniy7K/lv3jRAvlqxrfSsmULLNSDWxF/bgT4csIkva6hXv065fL/4Ycf\nZe68n3DCQrPfrf9lqGnS5GnSrGljnHpQTeEp7v/ChQtl3vz5ssbq9bEBAm0vB//58+bL17NmS6u1\nm2n/VjX8K/r9R/4Vq38R/98f/xnfzJSmjdZSm8HNAr/G/s6YMU1atmqdt79qeXMW2exS5knbnKNA\nAs27p3Efmtl/s95mzUOuF1a/HPsf+Rejm0Hegg5hkoGEiH+Uv6h/QT+gD9H+ZG1FxmJ4UP1of5f5\n+yOOPxActyTJCJMLuAgliUiI40+KWpz/RPsbxx+zDmYrMhbDg+rH8SeOP0W//8XxF4oTx99kblFO\nwE1IkoWEOP9IpSbOP+L8I84/zDqYrchYDA+qH+cfcf4R5x/52YYrSDK6LhNYhgIJcfz9/3v8nTRp\nkjRt1kxfdEWt/8+YMYPr25Q2/k8x1CCEj5sGaMrgfORDRL/INjJEwgkKJGFpSqw7Lc8MEptBYK5F\nESrzr/qZ73zdR8qv4F+9eg1p3Wpt7EVA/eXwr4z0tm1aS7369ZbLv169utK8WdNfxX95/ef1D23Q\nrhrYkLC8/teoUV3WWKM+oLalvfL6X6tWTWmzdnPbcAHUVjX8l9f/lfX+I39qWHDlyD9zlyd/LPVH\n1//4/n/+/atkmImlgdWo297s+w8ZkBUnDiko4qU0RYXJ8iw9k+t5JnBpOa8y8g+gOiCp/oWMiH+U\nPxeFRMkyGhaMeZKFQCY36l8AhvqV+YPU1S3aH8MnzL0Z8fE/ZET7E+2Pi4L5MC8ZCxPtj9tYoGO4\nZNDxvDj/UXQSZKL9NV2K44/hEMefgEMcf+P8w42jiUT8+zuPBwfZZBwhRD7GMqiQZXI9L46/ik6C\njEMaxx+VmPj7lwtEHH/i+JPKApUjjj95POL44+NsMJ0+xlJWNCkZZSg8RhTHX0UnQcZFahUef9nE\n8tafVf75m5j34f9o/Z+CY6f/AzVc4sC4MtXf47hAjvRCGTYfMJnhjPj5T73eRvqkU8eyqISL7CEB\n5wVYHchQoWVdWl+oIPKP+KusQB6i/AGJqH/R/mBQ/33sL203ZUo1LPGz9tftOSmM1ohpx92lIVbh\nlVluNk8ZWCVeNPInEg5Z8CP+ACIITlaeTHQMpCh/qWaloah/WXmhamWxifbH9SoYGsfHo8GP9sdx\nyutTtD8cq0xIov1NLUsaystLtD/R/mZlI44/blfDQBPHn/j3D42ki0Pw4/zD9SQ/nsT5B2XFhCTO\nP9KRJQ3l5YWqlc1TRTMhYpa6XDTqX8Ak6p8LTvbvaZOVqH8Ukmh/UsuShqL9yeqLyolaFH+4XQmG\nlnLELI8GP85/HKe8PEX7Q1kxIVnp9gfgV+j6KzRFbzqgvnB3RFCdTCLUJiyOsqW2jcA1C9TcaRCc\npfLJXoUNDRomvqyHhEGV2WumMSU8Iv+If5Q/ygA3CKmyAI6of39u+0P7aLb0t9hfylSQKAZVruhp\nWj6DyRlakz9NhC3PkTLRxFWz0zxLtM2ACAeaNJ/lLFHT8hnL1JVubIv8c1ARqYAtg2meJUb8A0BR\n/ghERj4QifqXYpIqDoFJ0zUU7V+0vy4gcfxxJFQ1+IjjTwJFik0cf1U0FBBgEWQkxYdyY4mals9Q\nPNOkaH+j/XVpiPbXkUiMTtAtxtM8S4zzf6ICLAJGKT5MtkRNy2ewUA7LqH8OUNQ/R0KFhI8gWwym\neZYY9S8AFPWPQGTkA5Fof1JMUsUhMGm6huL8L44/LiBx/HEkVDX4iONPAkWKTRx/VTQUEGARZCTF\nh3JjiZqWz1A8K3b9scSOR+BuDN2ToFF8nYujGWwUtdsdrGd2qALvmrAJF1LxcT83Kphj73iqAnzf\ncEBCLqpx80IZ6aysGtpQCT39Glir4WkBkX/EP8pf1D+YAm7PULvAx5/R/sCa/g79VxMdBiKiGnaI\naZCPdDcebTgZKlP4NM7w9GGW3nM0leQJrYc10cRXx4JAEvkTIHO2Q89jEf9ENqL8mT65lkX9i/aH\nZoLyEO0vUXDNUFTi+FOMiMoJsUlG4cSkZpCL4w8hSlyc/7hsJMISsEFcs/iI9ocoOFIEyH7CyKUo\nTsyL+hdQSEQqg1O0PwQncdH+uGwkwhKwifYn2l+KAuUjjj9EwTVFUdFBJpeiODFPs/hkQAtm6KL9\nJUSJi/bXZSMRloAN4prFR9Q/ouBIEaA4/1MU+AjO5IQRSpI+E5HKIBftj6Ljj2h/XDYSYQnQRPtT\nEfa3otefq9Bw8D89qQCyoYcn6AYCCARWxMzw6lYDNTRqdpROSxk9REg3K2hNjJhwcdOC7spQn2mM\nk9YClm11R/4KasSfC9FR/qAgUf/+9PaH5lLN5m+wvzS4oQ41u6wzBIJFDlabqfzDA7yyBJmyTu/1\nWNyeVsTaWQ4DrdvLZatnWhgBNBT5R/yj/AVNoWqpemU1LeR5lhEElYv6p0gta2AStIjestksxdRo\n/6L9jfY32l9aCTiaBTUo+giWVnP0kU01mxLtr2KyrIF1IBW3ZbNZKtrfOP7E8TeOv3H8jeOvDhNx\n/OWwqAOqPuL8I4iFe1lU4vyLqMT5p8rEshNMxcblZtlslorzzzj/jPPPOP/8M88/aQUrfv0Vnx/D\nGKsl5xYBBP2UBJ0Z01DjH/8HncaSXU4hHakFPSEBZUlAh0BCxih2IXDTgU0iyMPzWRaFIn+gFPFX\n0YnyZ/qgyqQaF/WPpibaH1pK2FG3ogSF//9v++t01C86m3oxpNY32F+rl3lqjzWqHEmovDKeJqVx\ns+0qskxkWW9mIAqcGIv8gbHBE/GnhBkYhkiUv6h/Kg8qDtH+FBte1RfqTHAWj/aX4hLHnyAUCoZL\nCHwISRx/U82J8584/7DZRpx/qVYoGIZInH8BkcR+Ep1gN/JeYlwtOY6/ClnAKMUvwIT0OP44OPHv\n3zj+xvHXRps4/qpVUDAMkTj+AhEdTDh2EJ1gN/NeGFg8N46/ClnAKMUvwIT0OP46OHH8jeNvHH9t\ntCln/K3o9VfsJfDz0XXRj/a/xD7NhTXjrjs0vYTHpiPEIP+FxUGaO3bM1siU0hKYAacmgPUhzIMT\nWC/T9IfDMizAI8IFNktFmJsjSB/5Ez44YkPgIv5R/iAJFAXVo3R6gWjUP4JgmkIvcbQ1ak/g/dnt\nD02q2pKASTh+ATGmKlL21GAGRAU0UITkbC6rs/hyMkkA5/yXLl2q7+Tn+JeV2jk6VpL1Lsu/tLTU\nsjWXwWX5Z2mcvxZiH3NjzPL7XwgntmRhCZy0Kj4sviz/hACBFeG/xLHxvmgFrHfZ/mfrXhH+S0qX\nSunSgNkv6L/1bvn89dqlpJ3a1GzTkvCK9F8xzgKd1Fs+/7KyMlm6ZMkvwl9l4nfof1a2VgT/5fV/\nxoyvZebM77Snv7T/Du5v4c/3G9BdKfqfdHQF9e9/yR/rW1n9/27mtzJ9+jToEWxYxq0s/qGj7iUt\niPwJhaHgXgJOCCxP/6L8R/2P9k9Hnmj/aSt+wfzDbUy0v0Qi2l+VhwCDy4b7cfyxkVbx+B3m344r\n/ah/KQquhll8GI7yF+VvVfj9R+Uy6v8K//5k1q383z9cx6P9IxJx/qHyEOcfrhY5P45/cfyL418w\nDv9j/K3o9XdKKla87XXpZgHbKxAU2hY/uQmhpMDLGTCW8sSDRN3DYBkSdO+JG0X3QcslJuOA+pDO\nf7YzAb6WjfwTSCL+GfmK8kf1iPr3Z7Y/7Pvv0X8IEjQrmFsa5IyziNr/XHpKwjaogxdCaeb/CM35\nfo6cdupp0qhhQ6laraq0WLuFDLl4iCxcuDCUNKZ33jFMtt1uW6lcpYpsseWW8vzzzyc1k/+nn34q\nvfbuJXXq1pUqoNlww41k+PDhCQ0Dn34Gml6gqec0G8qDwx8M3V3x/n/xxRdy/PHHS7369aVxo0Zy\ndP/+Mm/ePO08+7/PPvtIu3bt8a9d5p/FjzrqqFybSG89XJY/F9ZvuulmWXeddaRa1apSF+3u27ev\nTP/qK63j3//+t7QHj/Zt2xqftu2kbcKzvbz//vs5XuVFvsOid5vWbWTI4MHJ+//hxx+A34ah7e0D\nD/SlPf6Bx+23356rKvv+fwIOg1EX+1+pcmXZZeddhHhl3UWDL9L6yWODDTeQjfCuNtxgAxn55hsG\nRk7OLJKVv5mzZkkj4H7EEUdotVn+xJObAvbZZ1/ZZttts2zLDZdBdoZBttoBwypVq6iM3THsjgxt\nnv+smbOkYYY3Ccmf7//EE05M5K9Hj57y6quvZuopP1j8/ucvmC8nnXyS1IUcN23aVBo2aKgydvaZ\nZ8mS0iXlVlLcfyfiRs5HH3lU3h892pOW8Yv5qzD+D/yLK1ke/2K68uLkv2jhImyeK5ELIRcVwd+6\nm9c/x2706A+02Vn5y/Zjwfz5sk3XrtKwYSNp3ryFvD5yZDb7f4ZXBfzZf+J/1jlnK/6v/ec1jb/0\n0kvM0T4sr//M/K3vvzz8lalxtmcKVJoVQpE/wYGDF0IWX4FnCmte/tOi8f0Tiyj/BCGVimwo6l/U\nP5WHaH+i/c0ahhUIx/HHzWocf9WKcozJjTNx/kE1ivOPYrkgKubi/CPOP1QS4vwjzj/cKKygH+cf\nPtzG+Uecf0Bp/sf8K4w0IDR54di7Mtf/aeCqULeTlxXarGloDDuAJsEP2woY1P94YYPmMNfK86kV\nISF87Kq0mG0xWWuwKrExgbRauW1SYC06KyOdOT05QUmYF/kTF0IW8ScCUf6i/v0J7A91nubwt9g/\nlldnNtTCZpFZudkUpNLAZF2w/5oB/urU/lgw1JAtkYa10RY9vO/h8vTTz8iBB+wvPbffXp5+6ikZ\nPGQwvhCfKTf+7Ubl/wzyjx5wtBxx5BEyYMAAue3WW2W33XaTkSNfl65du8mc77+X7bAAPX/BAjka\ni/6tWraUYXfdJYcccohUxWL+fvvtJ3PmzJFtu20rCxaCpv9R0hI0d991t/Q5pA8W/KspTdrF5fef\nX0L3Qb2TJk2S6667Vr7++hsZNGgQvpKeLs+/8ILCxAXuatWr5yAbM2aMjBs3Trp37x5emvXfoTPL\n7RAZ/xuuv15OO/109LGrnH7GGTL6g9Fyx+13yJgPP5S3334bC9d1ZNNOnbQQ285S3Mjw6COPaJgL\n2+W6gD/bvt/++8q0adN0EdL7X6VqZenQoYNW6GMKI88+96zM/XGu1KlTB1FUogXw8E4gOPiii+Ta\na66Ri/9yCRbTG8v555+v7R8/frysvvrq2pznn31eZn43U7bvuT3er/WVNrtOrdqhuZbGTpQnf6ec\ndJJ8++236Gs43SHDf/GixXLWmWfIP/7xpGy99dbldl/BCTm33XKLHHf8CdKieTO5+qqrZeTrr8uA\nYwZIZWyo6Hdkv2X4c8PATPLW0zhCO8H/bzfcIDfdfJMMHDhQNunYUW5GvT169ND3tMUWW+TbwWLB\nedP9/R966KEy4skRsvtuuwOfntiIUCqU/6uuvkq+mPCFPPH4E1ZyOfj7OySL19GXAw48QF588UVn\nF8qm0WL+KQFqWg7+SrMC/L0tKbcQKqf/pC0kp6CQYOXyd/ythYUEuxccu+LOhP4/h81Rb7z5pvTv\n10969e4tW2xu7zr0IHS4yCun/8X8V3b/2b2ycFpKnTq1VXfqQs+Tfiyn/9pOFyLQOFlSrqjrGl0F\n+1/R+Ef+FTP/cNGN+Ef83XbpwFfB449aUtjJ8uY/ZkORqQ2m0Q0tZzDY22h/UyyWGYLi+JNA4qJD\ntFx2ovwH7YEX9S/oUSocJju/8/w/yl/Uv1TEov6p1kX7E+0vlEL1IlWOaH+JQBx/UsHwSZzLCuHB\nv2KRIWzqmBmcFyV1Sh/tr6IX7W/e/mblK+jfyl7/p5BW8c0AKsM8CcElF9cGlGBBgSdYM8nknE3E\n8gLTQmJC71oAv6QSNiIggwun+PgfTyvNmjTM6xuYgRh5JNmRf8Q/yh8Vg0oU9e9Pb39oL2k13UT+\ncvvrZV2tKFrJSITElENg4oRhNuP8tZy3Bolu/7Vxlpk+w0L0hIkTseD6NBbG95eHHn5Y87npoGOH\nTbDAe7Nce+21qKggRx3VXxfJ7r7zbu1sbyz+NWnSVPO7YFPCM88+K99goXjo0Jvl2GOPUzD2P/BA\nadqkiX7Vz00Jz4Lm2+9IMxQ0A8GrRA446ABphnpuw5f/pEmc/xJVTv/vuvtuefudd7DQ+5LstNOO\n2uMq2Phwztlny4cffiAdO24il19+hbbT4Vi0aJF0wuaBttiscNVVV2mfSODYuV/M/9rrrsOX97X1\ni3ue/sACtbBwfz3S33nvfenapTNOehiegbggDz/0iDyCTQl33HEHTlhYN6kyFwCmjz76mPTjCQ9z\n52oW2+Cu1mq1M6dMWOt42sFDDz0kfbBofvDBBztp0gcmjB37MTYkXCvnnnueDDp/kLZ3o402ks6d\n0c4HH9TTJXhFx7vvvSOnnXY6Ftqv1rYvr//MTFECAxA+8tij8nfUxRx+3Z0tS/z7HHKojB03ls1R\negsUPYP88ZSEc845RwnH46SNmjVryunYBHLA/gdKf2xu2XXXXSFnjZFvQs9TB9gPdbC/3rpZOLnh\nPGy+6NW7l9yCzQhsVHdsSGjfvr089thjssymhMDf2+7+3Lk/yYgRI2THHXaSZ555xvigsrPOPFO2\nxOkgI54YITO+/lqaNEabVkD/8ledhOroLYd/QvEz8h+gWCH+uZeTVF4ef58BBuOy0vnzTWYc56ac\nxMLp/NOz/EVZhqbS7tCdgXfUHieJaE2gWxH759W5rxXxUQH9ZxschU6bbiZvvfWWppgS+XtJSEIH\nc7nIDD2B90frf/H7V4FAYs7CZF/UCuifwxGATL1VTv/8zYcmVoD8RfxT8agI/Y/4R/wTBKL+m0GM\n9j+Of9lBPI7/6UQhzn/UXGZFIs5/Axrw4vwf4pEXjmR4XfX+/k7V2oRaBz5NjH//ZF5iJmgCXvyK\nAwG8KP9R/qP+QwZyf1gGExh//1Bg3Jy4H9CBUVlF7S/X372RFbT+SPa4vkF3B1hrGExahQD+5yYE\n/meOQxj+0ygyQZ/mgSKQlbFO4o5/rM4GPtbAGFwJe2/hQuRvmBCOiH8iIhoAJlH+ov6lNubPZn9s\nE8Lv138zNdmNaOSgjvaHQTPLlhaSzsXC7qhRo0IMJFpElTOhywa8ztLSpXLKqafKySedmM3G1/8d\nUUlBfsQ1AmPHjtev4g/tc2jCv1atWrI/NhFwkZYLr41xdPoJJ5wgvXFkPx35N27UEMfsN5Rvv7FF\nw4a4HoI0vFrB+TfG4m4DpH/7zdc5/j/X/+efe15PCujZs4eWYVcP2G9/Db+AjQp0xf2/4q9/1VMS\nhg0bpsfyO3+FCfTp+KfFkVDQ6yv41fy1196g11GEgVI6bowTDOBm4doFOq+D4W++/U5PlOjRs6du\n5MhlkiA48udpFGvjqoyRb+DKhGKnk0ZLJC1PXzj6qKOxQaKO/O36GxLqF/CV+JZbbiUvvmBf4r/6\n6mtoZkH2x9f5QEEbt/VWW+NY++Y4DeNpLff555/r0L5pp021edywUdz/r2ZM10X4yy67FLWEHqK6\nGd/MkKP69ZezsQGkYYMGziJpzz333CtTpk7RjRpdu3YJGxuT7CTgdX799Qz5EZsyDjvsUN2Q4AR7\n99oTdRfkjTdGJvxnfDVD+h8N3meBN2RGmePJ1tVaraa88sorctWV2HACx/e/aNFCzVyyOL1y4dJL\nL9V+zcAJFXT+7rz/CxbM0/SatWqoz4e39frrrpeLL7lYFs5fkORN+PJLvaaiHk7E4HUWfXHqyNff\nfKP8R416S4459hjwKJGBxx6rmL2JL/q5ueF9v5Ig1NSl89ZyPU7loHv3nXdly6220M0U3bp1wxyt\nsuy1117yEmWbDS5H/1mO/e/cuQvoS6Q55KpPn0Pkq6/tmpE0v7Plt1gbm1v6yIwZlu/9/x5XuRyF\nU0zq1qsjLVq0kJNPPlkWLwmnYZBvEf9h2Hizyy67yt8feECvA6lbt55uJhmPa1q0oQHgR7ExhP1m\n23ilynnYMLMQ78fxPxqbnoYMGSIHYiMTTxc59LA+cgw2R9ENPOZ4xU77XcR/EDaiDB48ROth2VNx\nDQ3dDNiTATjZpVGTRlKvTj3Zcaed5L///a/m8THqv28D4y3l/vvv0/e2+WaddOPKueeeazTQvxkz\nZmibzznnfEtD/4nnlltsKY8//rim8VSZ7bffQfj+2bfNNttcN1+5/bn19luld6/euvmH/eLpMj/9\n9JNMmjxFjhl4jNTDdTDE45677zIe4eW+P/o95U0ZovwdC/m5eMjFctU1V+n1LayLMjEZp8U4hqzg\n7rvv0dNBSiqV4L3sLPf//X7ZCvZh8pQpoX7zXKa9rL//hKjI/mh6Oe+f6V6HlbWY978o00jwjPwN\nJ8cu4l9s1NK4y0p5+k+BcgxNuKL8EYeof4pCsXCYiBCfIDUuO1H/Un1TkKL9X0ZWov0BJFSYYlFJ\nkGIg2l+iEO2vouDiwEjORftrehLHHxOLOP4WG9U07roSxx/IShx/4vibqoYaD7ehbknox/FXUYjj\nb144TEQoH3/Uv/9WkfVnNANaSHCDQU6/HNPtBwpxQTcRBMyhkdxswB/Dua9AdVhfjkf4SvCPmquZ\nLKcE6jOJP7BantFqNkmQGfk7aBF/IkChiPKX2cIU9e9PZH8g/r+r/AezS4OrZsaHT7BJTHRqf5z6\npZdf0q/hueg16q1RgbT4T720nFe13rrr6WkH3bptw46o+2nePLnvvvukebPmsuaaa8rECV8q71at\nWxlB+MGyzTqttYlz5szGot8OetVDYyzMMpH1sx3ckLD5FptrOZ5qcOONN2IRsGHSlbfeeEs3PGyB\nhT531krUoIFl+//ZZ+NlvfXWw0aByihi1C1aNtfi3+ArdufPVjB3/PhPsXB5kRx00IGyzTbsZ6ZO\nLcWHI+I1lkiN6jVw4sJlWKTtZ1SBZPjwv2t84403gm/81Ufw7LPOkrlYZL/pphu1Ts+1Cjxm/Ife\nPBTXQIzRExwsP0uV7/99WDx99bVX9XqDNda0KxjIc+asmVjEfhs+Nkigep6mQMfrM7z/7Frbdm1l\nKjYL0H388UfqP/WPp3RBtEaNGlgA3Uree+89TWcrFy9eLO/gNApekeHI0NYfM+AYadq0sVx44YWo\n3sc/FkMp/N+nTx+ZPu0rOfigg1AO+LOyxHkkgz/MJl3lynpLlYZJtXAhNhTAcWGY/JU3FnGbNG4i\nF110geYpQzKFW61mDenZo6esu+66Mu+n+fIy9OHEE7DRBtncBGOuIDwZhP1ajI0YeWe9bIjNNV1w\nVcdTWGzeZedddNF6xle2cN8FmywuGHSBtG7TSotOnz5NNtl0E3lyxBNyIGRr4DEDQX+/bIGF6QUL\n5kuDtRoormxhZ2w62BS0P/zwg/L/KZyO4W0YNeq/MiUsHP8w90d59913ZX+cXtK0aRO57bZbsag8\nWXbGIvNHH+HdJQsGrNn6z80FO+ywg8yePVsuufgS6Y0TI4Y/OFz22AObO+BmYPNHko+NFb179ZIH\n//6g7BnyKSt0N910k/zr1X/Lcccdh00nDeVvf/sb6huieYEkx3/6V9OxWeIFbCI4TPbacy+5+OKL\n5dGHH5W9994L72Getu6ee+6RA3A1DDdAXYlTSrbccgu5/LLLZJ/e/l54wsc46OhgPWGkVevWUh1X\nr3Axna5z562wSWpTb2KOf8dNOkon4EoU+P4322wzmT9vAa5o2U6G3TEMJ4ocIuecew5swCd60guv\neSBmP/I9vP2O8Oqa+vXry+effyFrrbWW3IzTYUqxAYjMRr4+Ut/V3XffiRLgAIheefkVvJt3ZP31\n15d///tfstfee+vmpNPPOF03DXHDzx577CFf4SoZFpg2bbo8+Y9/yFlnnSmtW7eSb7BhhXZrX8jk\n7bfdLqeecqrS98NGH3fsC69ooZx+j2tx+GY+gs5eBBt21plnoR9bSi+8P24y2mdfxxAnr+AUkX79\n+sm4sZ/oBo8ff/hR+h7WV0+VWYgrc1xW6Nvbdo700xSTKMQ1kKF1knLkT4mtoAaN9H+PP2kLvPLA\nlu2J/AFPxD+RDA9E+QtqQwXJKF0maFBF/XNIDDCPZXQqIBntnyuXSxTiClcGKyeJ+hf1TxGggKQ6\nlQ1G+0OAov116VBxyQiImxJLN6w8bGWi/Yn2lxIRx59EVzwQx99gKuL4E8dfH2HgZ4KmKnH8dUhM\nYTyWsalBk+LfP25cXYwQV7gyWDlJRdpftKmi19/5+7P+Wm/qRVTw86huGEgQ0jRuDeIeA00laPo/\nlwXCr/66OkACpnkR1IW7H7gBgfWX0QedxpBOPro5QWn4s6xt04j8gSDBVkefYEf8o/xF/VOt+NPZ\nH5qAX6//ZkdoX2l5gwyZSbEUhG0hMmtzWMrjafh5fDnPfzvvvDMWji+SLl22DnRaCQnh0nIF2nvY\nedow8ucq8nG4foHDxeU4XYC0XCAjxeqrr4EnnZW3eIl89913uqCX6r/oYtpRR9uXzvyqnvV7Ofe/\n/+F7Oeroo1ghrl44K6HQ2kFu9bFNLJqW/w5H9W+ySScmsqi6KpWrSm3cv85Fv0wyggVco3C71nHy\nSacE6rRccf+Zo7nL4X/nnXfKS//8J9o9QFq3bhPqo1ci3+BqinvvvVevHNhg/Q00j/ytRveNls/t\nttsOODM9UIRmlcf/+uuuxekQ+IIcJzeEFqp/0EGH4IvpvfWUAeLFRUw6fklN5/zXXGMNXYRl2scf\nf6x9fPe9d+UQLNpyMwJPXODmkS+/+FLatGkjLdduJd9jUbN69WoJ/jwFgQuhb+HrbV6zYO3gnIDO\n5H/zzW0DClPCTIJBuGX7z9TGuJqhDt7bvVi45lfga62xlpQuLZW//304ckvQhh+06L333g3ez+BI\n+zexAaGW5hn/AFrgz9hfLr1ErrjiCqU57rhjpds23RA2/lx0Vixr10EaUovkn+WffuppXTR+8aWX\n5EVsbqAydOy4MTa19MHVEqdJVVwVQnfppZdhA8o8LE7/G4vg3ZUDv77novQdt9+hpwz073ekPICN\nCof37YurRnaW5194QbH3Vjt/xvn+6HT+heZutXVneRgLzRT9Aw7YD6dqtMa1HOdmrpXwWkTef3+0\nluXVJPyCnq55sxY4aeJ1lYn3w4YTXtex1562UaF5i+a68E6ZWa1GdYWTGxHexikCa2Ez0pCLl0gN\npHNxHq3DP+eX9S183nnnAfdLlWKTTTbRr/VvUwxOlJNOOknaYhPRG2+8ifrsBIpaeIe3QS9ffvll\n2XHHHVEO9aMq8uZVG5yXvvqf/+BL/wd048BOO+/ILgWX8t9//wNgf2biKpcX5VS8m7VxugPf8Wef\nfgZdvA8nV5i+HNLnYGnVqrWesjFy5OvWE1Szx+572AkihTIZhmthXsD7GfvxWOmw8cbyCt4rjKNu\nmvocJz+0bdtennv+OWkG3DbYYAPlw0Y/9xzSmjVThLh5ghs6PvjgA00z/SvINcCd16UQxbvuHCaj\n339fnsBmll5791L723a9dWUgytGxd2YWEOIVJQoNS5bgPb+HzS3YoAFXWlqKq0yGy5w5c3RjRT+c\nNkHdHTdunG7qOOecc3HaRTO0nxuWrLwWtN5r0OWPTH7r+GP2GtX669Gg83WfbFOCyD8//hIZRQdw\nGZ6IKXTl4ZfiyFIR/4BdBhbTPyaUhx9Sy7H/WjziH+UPgqAz8qh/AKI8/ckoWrQ/0f6qvvgABl+D\nLjfuW6pm4hHtbxz/s78/0KKoVYG4xPlPHH/i+AttUNNZnv1UTQmmNM7/498/wXZmxCL+/eN64z7V\nJQUozj/i/OOPNv+o6PV3/iaqv/qX0eLq6AQP+wzM2FDR+M/UTFUNBVKX/syoHUEGfnvV3yfxm6/+\nnVmi9zIzo5LFtTAzefIyfP6YiXDkT1yBBV3EP8qfygLlwWSC0sF/VNjURf3TPyooLQGXaH/MpC5r\nfw2pRHYgRokkIUBJSlwwQy57TE9yQ4CLdDw+f1ccrf7mm3YEOOlssSv1bUBmRgkWg5fK8diQwCPN\njzjiCDn0kINZRKpWq6rlKuFIcHWBv8UL+lU9070NM2fOxALsTlgcG4tjye/WUw28/c6fC4k77biT\nfPLJJzhy/G584d42Ka88UJnXx4CXZ161KtVsbAq6p/R4kH7R4vwX8Dy6/w58Mb3+hhvI1vha3fm7\nn+1/wo8VIpLEESB/fvF91FFHSYeNOsjVV3LDRvIiWAJfKj+ihQaEY+c1MdTi7Xe+7if8Qae/+1qh\nHP/RH4yWDz/8SK+D0M0ACVvsWMRX19yAUKVKFeVUjZsI4CpVSjcLhAQ9wYF8u3btJhdgwwoXgC+5\n5BJdVL3rrnu0O9defY2S8/j3ergqoga+WGfFEydNxlfYR8qZZ54pW2/JjS50bEjSmNBTzdBHusxJ\nKkPT++0+2/nXsPmlwZoN9GqI1q1b6dfonONUr1YdR91PkiOP7A/eZ+jX7mSZzn9Sfv6+DjjgAPkH\nvk7vhdMCbhl6C46+xyabwJ/1cXOHxxP8If9efo01Vpd/47SAjz8aI1eibT179lT8z8UX97vssgu+\nsv9Rmb7yyj/hm/y/go0q/Of2365SYTtZa5H9Qwr1ny7hT8oESgvstcfuWpqPevXXwIkPveU/WKhn\n/80lAdkAX+7THYprMLgJ6LXXXtMNFE9hgwVPAmiPRXS6PoccKmdhAxCv+TgDi+TcZMJ8vSIM1e0M\nnVxrrTXZZGBfVXbHyStffPlFgpdWkrBNArAzu1hbQcANGrxeY/To92X8J+Mhdz/JgIEDdUOCv/c9\ncKoC+8HTAKzOSlIHG0W4IYGO8pedf/J98dSMMWPG6OkiH8H/5JNxSqv0LBNiihHCh6j9stS1W7bS\nE0newFUpS5YsMfzBf29sCjBXSftO6v/85zWt7LlnntbNJMwfiRNdlsI+Pvvss3Lg/gdqkZtvvgm2\nb5H29QuckPAc8rjRh27+/Pnq+/vfdffdNc4+j8aGBSLXC5uJ/P33PewIxSORATREZZyAIcz5d8OG\nDWxDAgvD8eQNFuJ1EFMmTxaevrHHnnvohgTmV8P74+kVWpOPv6GsvwfnT/vPvidO+YcYwq4vmhLq\n0AYnBbSZmZgHrVYv73zdj/wD6hH/KH+uMvSj/qXyEO1PtL+pNGSGnWQgUs0JljSrRaZIeMbxx9Dx\ncdf9OP4GqYnjb1bD4vgDsUjsCQJuP9S4JGYnCWhyQq8xf1iql3e9cz/qX0At6l8qbxQdwJLIEwIu\nPypVidglAU1O6DXmD0v18i537kf5C6hF+UvljaIDWBJ5QsDlR6UqEbskoMkJvcb8Yale3uXO/Sh/\nAbUof6m8UXQASyJPCLj8qFQlYpcENDmh15g/LNXLu9y5/4eUv1Vg/VlXFxLA8eMi5BcvCS9ENxQg\ngv/L9BUCeqKtiFu6vVmm4yUhqVL41ZPrS/pKNY7aQjr6qxnMC0WYYtVoIPKP+FM2IB1R/kwzov5F\n+/Mb7a8bWLXJeKhPexscRCxxtD9564zj9vF1OV0w4wzp/+/rgvYHXr2vmSa+FgK3xUsWYxHvELkF\nR8X3O7Kf3I674p2oQYMGSvbjj7YY6/znzOFX+SU4oaB2aC/uYcdR9127dMFi47tyx7A75HBsbqDz\n9nNt7CvQbNOtqy5IkuaIIw5PCFak//xSedbsWZlaRZaWlumie11+Ae8zDlA8/8LzSP9RTjz+BDbD\nu5T4mqi2LAkptMX4X3/ddVgYP1I64Xj4l/75MhaJ67E2K4Qn6W/Hl9+N8KU5F3KLnVOGtcFl+Ks9\nDfWwsiz/+++7X6sjfzrHv5g/SzXG9QN0P+Ho/Kz7fvYcachrM9AQbhgZMmSwXs1hNAXpvY8tzo4e\n86Eyz/InzfHhK24el//kU//Aov+TuKJgkXw5YaL8A3GeqpC0OuBPXtbWtKXl9f/YYwfKLbfegi/R\n28rDjz2qx/Y/9PDDikH9evX0y3O2oWPHjuD1lPKfj+sRJpD3P56SH378IeBlrd4UX+rztIARTzwh\nW+CqgFtR9wJfJA4vwtvBerXdIb0MbZ8DrFjThhttpJswXnnlFf1antcp/Atfz7P/LPPZZ1/AL9Fr\nFXbAYj6/+N8zLD5/+eUE5Hn/w/wLcZ9/FfMvfv8s202vGmHI8GvSpImB1jaxAABAAElEQVTMxQL0\n9+ivtjkjf63whfzDjzwsNWusJldeeSU2BvSQNdZcQ4YOvVnLr9O6jTzy6CNSq2Z1uerKq3DVQXdZ\nHadn3Kz56fyv2dottGp//3VxigWv8gjwaF15+UMO/t9cr1oIpYBhy5atZBw2DXzzLU4uAVbNmzbT\nst7v7bblNSoi03AFBvM5/2zfvr3izgdr4j/nS/+aa66Rjni3lMEOkIXu3XsiFQ6Z/KPDMZw+bZqe\nGMCNOu5Yfqedd1Kqb7/9Fu/BctZuif6SE+ItcMrCRh064DqKl/Saj6mopy+upWgD7P6DTR68UoP4\n74bNIixD+9e//1HCjUDrtW2nJ2Q8i9M8ck75FKQFrsKhY/8/GfeJtGjeHNMnndojtSCr1VpNTwwp\nI/1y+t9Sr2RBdqizZq3arBEbFsrkM5xwQtcFp2tk7d+2uMaCFfq44Pi7r4XIUOtU1vpASs6FbE1z\n/kkhpBp9KBX0P1uBl3e+7htN5B/xTyQhyh/UIWhSokKuP0yI+qco4JGiYngF1KL9IUA550i53XXf\niIBbIFAE8QhIJnV4eSZE+VMU8EhRMbwCalH+CFDOOVIud+4bEXCL8qdQqAThESQpwdDxY0LUP0UB\njxQVwyugFvWPAOWcI+V6574RAbeofwqFShAeQZISDB0/JkT9UxTwSFExvAJqUf8IUM45Uq537hsR\ncIv6p1CoBOERJCnB0PFjQtQ/RQGPFBXDK6AW9Y8A5Zwj5XrnvhEBtz+K/qGdlH/9rbMC11/TX1aB\nYAk/NVYE0TL93JZpJoz81olfJLPRdg0DSEDOF8AfbUtAv7isFD+szsedzfii1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X8XXngh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\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c/5NvxCVx/SkiI+VcUDCN8sxifyVMqsg/2WfZS0pTPPJP5B/zgMZFjpjIv8kz4pLIv0Vk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Ao7iSgiTbSZlKh/efypPn97/MX4BtPw8cfHH40pzf7+ikHExx8ff3z8yX8PpGE3EeIoMZKkkVcJ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\n/07v5/r9X8e/0PX+ryAANer1P/S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bIuz4+rtjhbRhIAo7QQDfRcTVd3jGoR0IEFvBvlFj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\n"
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IIIAAAggggAACCCCAAAIIIIAAAggkJQHHsJNl+4awGhzHRICla2KiRV0EEEAAAVsW4DPN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\nePBQsmdzCZv90o5VkEGfqX9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npHbt2hb5kZ1MnjxZ+vTp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\nD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4EwVw4++OA099xz+zmeFTAa4RhGBITiQMHL3I1yvIOEA0HiPucYBNFXQm3Qb8KpwAjF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+ },
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Cuse4u8Sd4cYf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bLA/POFff+5c5h3nlZW/vLrbgpDh31pxgAk4P1gj53/HrosOWnn5Dvv94lGCY+F3377r+Vv1mym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\nuaFpB7IFDWlbHbMCATR1hp4FdZxfc801vlkFi8go3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5xhGvk4bfrXtnb7tlavXr1oO/2D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\n9LT2lLVA5f/+7/+ia1LoHk1rIMuXiy66yJQpQVk+4kVBD3qeVHJlHlHwz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7I19+ijj/rj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3rW4al1ObPW3LIo6aHc3v/5MY/boTY9O8n8eS8y6K4uKpaMM5RulDPBMg\nQIAAAQIECBAgQIAAAQIECBAgQIAAgT0TGNUyNe+wZcnSqE3VEAZac/ph3fatmPJgaCumFx74cqDx\nTaVqzEPfr5l44NCXg8u1U0o/kizurTdf+N7B9QMLNZO3/ZyB9cP5O/bM02Lcu98WVSmAsKu2deHi\nbd7u7Sh9P1OdpnSoxCaUUImjps8ECBD4DQsMBBNOnzknf/IjS5+NPQkkHDFheoypr4+HljwTi9YU\npZbuj4baunj9oUfHOUeemMIHB8aJqcLBvQvnp6kdTohjWg7NqcCdneYjqbpCsf3JB8/OUzYUgYX5\nSxdG19buGN/QmHerqaqON85+7f87RHND+pK+zNqWp5+NhmOPjq5HfrnLnhVzX3U9NH+X2wy82X7F\nlbFx3gNRPXp0TPrby6PhpNdEMYVDnoOqP5RQO6kU+CiSmM3psX2rnTIpVn/xX6P20INywrSY5qF4\njP/zi2LDd34Y7Z+5NoZzjN6uru0P7TUBAgQIECBAgAABAgQIECBAgAABAgQIENilwOgTXxM1Uyen\nEsB90f30ony/u9ihL00ZvPzSvxrct5j6oDYFDLqfWxzVY8YOru/tLH2RX6xoTxV9t64s/WCvCCrU\npmBB7/qNg9sOXejtLFV9KCoSrP77Lw6+Vdxvr2keF5se3vW9/MEdtluoTz8AbJp7Xl678e77YnM6\nTtfjC2LCB/806lIFhEg/thxofRu37VvdUaWK0b2rVg9sUlF/hRIqarh0lgABAi+fwEAwoejBngQS\niu0vfu05MTZNy3DlPbcMVikoAgTfe+rBmDS2OV53yFExI1VEeGMKKBybAgnF1AvzFj+RAwyL1iyL\nj5x+XhQhg4H200WP51DC8dMOj56+3rz6/v5pJDZsKX0BvrhjRXxz/o8Hdon62lG5MsOClW2D68pl\nYX36gn/cO96821BC49ln5jDAnvS7KPG0/C8/Hq1f+2KMSnNuTf70R2PJuy+OYn1vmqeqenxTrLvt\nztjwX/cMHrZmQnNe7n7y6fx3VZq3qm9rby5TNfqkV0dx4TT23N9Nc23dPaxj1M6YPnhsCwQIECBA\ngAABAgQIECBAgAABAgQIECBAYHcCDcfPiQkf+rO8WdfPfpGr924e3ZBfF9MqFAGBTT//RX499bpP\nR/1xx8bG7/93rLvze4OH7unsjL50H7xq3AHph3czY8M9P8nvNc99dw4H9CxbEW3vmFtkHnKrSj+u\nLNqWRc/lv7WtLbHl2UX5fnrxedNuuTGqDxwfq6+6LtZ95/t5mz15qp/9irx5UdV45cc/nZfrjjg8\nT+FQvKiqrcnriqe6Y47KPzTc+sKSqJk4IeoOL1U+3vzUs4PbVNKCUEIljZa+EiBA4GUW2NMwwkB3\nn1m1NI6Zekj80W+dEZ+fd2es6SqlDIvpF46YVPrC+rmO5XHU5IPzLvc/91h8I033ULTTD5szGEgo\nqisUYYYFq9pi5YbOmNI4Pm+zetO6eGxF6SJh2bpSSnDi2KZoW9uety/2+8RZ58e4+tHxtYfvzlNA\n5B3L5GntbXdE4zlnxbi3vinWfvs/d9ir5gvOi9rWqSkI8KMdvr+7le1/d3W0/PN1UTNpYhz4/ouj\n/eprY8vzbVF78PSom3VIrL7uiXyIhjnHxKRPXJGXX3jnhTH1C9ekfSbE8r/4cHTe9I38mP6tr+R0\n6qhUKms4xxjs25C5vwbXWSBAgAABAgQIECBAgAABAgQIECBAgACB/V7ggPTDvbFnnBqRvpivHt+c\nvqAvfY3du6YzVn3uhuyzZeGi6Jr3s1wZePJnPxHF676tPWma4Vk5OFBU/h2VKgYPbZ033xbNl8yN\nce99Z4x5/SnRs7Yz6o9M4YBULWHN52/Mm/alH/EVrfEPzo1RM6bFiis+GePOe2sUUyVMv/Pr0f3k\nM6lq8MQcSCgqH+8ukDD1c5+M3g3bVjrYsmhxrLrun2L8+y+KUYceEpOv/Ej0be6Ohte+Oqr6750X\n9++3tpe+4yimd2i54bPR/asnY9Ssw3IVhc2PPBqbH30897XSnoQSKm3E9JcAAQIVKHD3Mw/HkSl8\ncFCqhnDlGy6I9hQo2Nrbk0MFtdU10dG1IR5oezKqUzWEOa0zUxWE2TE6VVYYXVsfr0phhoF24OjG\nWNIfOvjf538V56bKCkV74PkFA5vED55+KM48/Phoqh8T1/zehdHWsTKaxzTmQEJb58qyCyQMdHxZ\nKjU19frP5GoEa2+9I7r6yz81zDk6V1GoTknOYpu9bd0pPbn2ltvzXFVjzz0rhxs6v/rNGH3yiVGf\nPuOgO26OLS8sTRdDKRiSSkSt+/ZdOXm6PqU9m1IgYtInr4jiYquYQyuXy+rti433PxDd6UJqd8eo\nrSlVuaiffWQUidX2q/8xinSnRoAAAQIECBAgQIAAAQIECBAgQIAAAQL7t0Bfd3cGKL6Er0k/hCta\n3+bN0bMi/egwfQnfcdMt29xPXvnxq2LC5ZfFmNN+O0bNPDSitzc2P/ZErPvGv0dPmrq4CA/kY/Qf\nt/Pr34qq+roUMnhb1B7UGrXRGlufa8vVgzfc+9O87Yb75qV752+PmjSl8ehTT85VGJZ94CMx8YoP\npMDD4VF/9Cujr2tzDkSsvuHLeZ8dPfVt3ZJXF5UZatJjaOtN0zEU98XX3nxrNJ59Vv6c4v2tbUvy\nNA6jTzkp6tMUDgPTPOcKD6kyRDE1c1HKYfPPH4qVf3P10ENW1LJQQkUNl84SIECgMgWeWLk4rrnv\ntnjvnNOjddzEwQoHxTQNjy9fHF9+8Ae5osE9C+fH7EkHxewpB+fpGYqKSU+3vxANo+pyoGHWhNbB\nUMK9z/4yzj7ihBRmrEpBg8e2gfmHn/5HnH/cmTGjeXIcNqElTwfx6PJFcftj92+zXTm96N2wIZbM\nvTQa33BGNF/4npx87F2/IbqfeiaK6R2GXSGh/0Krt//v0HNck1KiY047OWqnt0bz3HfFslT9oP1j\nn4rxl70vV0OoT2Wnetd0xIbv3R2rr/1C3rXjX/4t6lJQof6E43J4oVhZzFm16prroyh9VTx2d4zi\nQmvzLx7J5bOKElr1hx+2zUXk0D5aJkCAAAECBAgQIECAAAECBAgQIECAAIHKEaiZMjl3trqxcbDT\nA+sGV+xioePGr0bxGG4rpiYugglFKyoO9Cxbnqsk5BXpafMTC+K5U84eeJn/dnz55igetamKQg48\npPDC0FaEGdredn6eJqEvVTjI0x+nKgxLL7wsBxRqp6fvGYYxbcKSC0rTTQw99vbLa750UxSPUTMO\nit7Otfke+/bbDO3/js5x++0r4XVVnHZJ8Z1PLLzp8mhpaamEPusjAQIE9guBJcvbo3VKKdG3tyd8\n6V2f3+Ndrz3nffHZn3w7nk9VBfakFVUQLjnx3PjwD3aeEhw43vQUTOjt6x0MGAysH/q3CBSsWN+R\nwwpD1+/JcjFtw+TG5ljcsWJPdtvhttefu+uLiWVnvHmH+1XKypqmpqhuGhdbFj+/0y7XpTBBz4pV\nO7xIKnba3TGK94vUa3FBt6dt6o/u2NNdbE+AAAECBAgQIECAAAECBAgQIECAAAECOxHY13vaB333\nW1HdOHYnRy+t7lm6PNreMXeX23jzNyPwct9jVynhNzPOPoUAAQIVI3DD/9yVwwUTxmxbWmh3J7Bq\n47q46cEf7m6z/H7b2vbdbvdiBAm6tna/KIGE3XZ2BGwwUPVgV6eyuyTo7o5RvK8RIECAAAECBAgQ\nIECAAAECBAgQIECAQOULrLz879L0Bh8cnHJh+zMqAgntV35u+9Ve76cCQgn76cA7bQIECOxMYMGq\ntmFVO9jZ/tYTIECAAAECBAgQIECAAAECBAgQIECAAAECI1ug65FfqoIwsof4RT276hf1aA5GgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgXEErwT4EAAQIECBAgQIAAAQIECBAgQIAA\nAQIECBAgQIAAAQIECBB4SQSEEl4SVgclQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE\nhBL8GyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgReEgGhhJeE1UEJECBAgAABAgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABoQT/BggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAIGXREAo4SVhdVACBAiUiUBfmfSj0rsxXMeqSj/RMuw/0zIcFF0iQIAAAQIECBAgQIAAAQIE\nCBAgQGBECLj/OiKGcZcnUSZjLJSwy1HyJgECBCpb4ICGsZV9AmXS++E4VjU3Rww3vFAm51UR3Uim\n2bYiOquTBAgQIECAAAECBAgQIECAAAECBAgQqAwB97QrY5z2uZdlco9dKGGfR9IBCBAgUL4Cvz/7\nJF+U7+vwpP+ws+NujtN00QW/3qJMkoe/7lAFLg0x3Ma2Ak9FlwkQIECAAAECBAgQIECAAAECBAgQ\nIFBuAtvcdx1yP7bc+qk/eykwZEy3Geu9PNy+7la7rwewPwECBAiUr8AJ04/Inbv9iXmxrmtD+sl5\n+fa17HqWwghFhYQikDDguKs+NrzhdyLVSojOL30l+jo6drWp94Yj0J/eLC6WCluNAAECBAgQIECA\nAAECBAgQIECAAAECBF48Afe0XzzLsjxSmd1jr4rTLkldilh40+XR0tJSlmY6RYAAgf1RYMny9mid\nMnF/PHXnTIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMEIETN8wQgbSaRAgQIAAAQIECBAgQIAA\nAQIECBAgQIAAAQIECBAgQIAAgXI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+ },
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7SQI/wRweAndIgHvJHYJmNwlaQH+OGCkhQZ9iDg4BBBBAAAEEEEAAAQQQQAABBBBAAAEE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\n+oDr7NlzrnIsIIA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w23XzB2ePdsz0ADbfPCzy\nyHdWzMN2u908KHSybnmPTpmwlczwwSHmm7Ie2eZbf3Zf5o/eHvm6Yh422G3mG/y3bItNhvnGn23P\nPHy8pbp5GGK3mRELbtkWNsMEEISYhyMh5gGOrWOmoAhbxLVuHvjaMmPGjHHl6YL5g7zNnzx5ss03\nQ9bbdfPwyqOcCZIIUTdNZmQCW8Y8oAkx32i0y2akgxDzgMRVR49NrycTCOHKcxb0+jDBHyH333+/\nLWMeAttN5qFYiAkaCNFjVwdt23wzM8TMy+5UDffdPKC37airmaYhxHyT0q4714152Ojaj3moFmKC\nQkLMFBwh5oF5uO05mWGvY83XNvW4zMNYp9gt72bkD1vGPBwNMQ/jQkyAwC1lNMNMuWGP13Hq37+/\nrac/N+bBna1jHqzaPC2jx6fXpy7ryzx4tGXMiBl2Xc+RmbbCXttOGTNChS2j1nqONJmH8q429Jya\nURxCzFDZNu+PP/6wZcxDVVc7ep7Nw05XHTOyhC0T9h/HS/dtAohC9Dj0fJgRKGxdzTMPDO01oWXc\nf56ca8pMeRFiHhKGmFFVXPvT+4N5YGTXzYgXHrtVY+e61A2vv/66Lad55sGoNdN9Ode9CT6w2/Xa\n0rZMIJRdd+4HJgjE9l37akbRCNHy7sk87Awx3wi2dfQ86z40OT9f2o55QBdigpFsGTPcuHt1j2Xn\nfGv/zANkuz9d1p+BU6dO2bImcMS2o9es9kd/nnS7ltPrR1NAQIC11jz9uTEPMe1252dP6+k2EyAW\nsn//flvW+TnT+tG5Dp1rWtvR868vXXbctJ3oXIdazj0550t/rszoNPYa1Xadz4ro9E3ve1pHX+qk\nfdLrSI/RPCB37c4EGdgy5hvuIc49Q+8NmhYsWGC3aR3tk543bc+5L0bnGnbtyCzE53USmZEJMHGd\nCzMlkeue5txb1VU/Z52fJ73eNen91zHTnwU9bv0ZdOrpvVidwib9+dB6zn1Ct+v1p3n6c6HJTNFk\n182oOCEmUCTEuXb03qQpOj/LEd1LbANu/+h1ovvWe6O2r/vUdb1fadLPR73vaZ6e02HDhrnudWZU\nJFsmvOvHBGDZOmZEIFtG/zFBiTbPBCja86ttqpOmqH4Go3Md24bc/tHPAOfzVT+X9V6j91Tdr/7e\noyk693K3Jl2LaqL3OP3dLuzLBM/ZfZgH/La8vus+9V4eXnJ+Z3A+j5z7jZnuKbzikebp57Jei9FJ\neg1qv/RnV1N07sF6b9B9uCc9n9qO83tobO5j7u2xjAACCCCAAAIIIIAAAggggMDdFtBnRfH9DCum\n7WksgoiXBSUMGzHaBiSM+nJcyNcTp9jXF+Mmh/R59z1XoMKCJf9n7z7gpCjSPo4/5Cg55wyeShAD\nYELlVBTM8UxgQD0VXzBhBkUFRMCsmEgmUFAwoeihSDhAkopiIucMEgX3rX9h9c0OG2bZBXbhV5/P\n7PR0V3dXf7unF7aefuq/+x0vo9jUp9Oaa4BrICPXQHYJSlBHgf4YrD/Oxr7UIZmR4p5UjjpJQoeO\nS+/rtxn+kB62555w9PP1x/74ok5xdSyqLW4M4fjF/nMidVJaUX+MV2dmfNHxq2MnvqgjW+1IqZMm\nvm56n12mBb+t0GEYW989Ie+XpdWRGVs/TCcSlKDOOh1f6OwO6yoYY/369Ul6VwkdSu4J4VDFv7un\nFKPOgtApq45sTYcOPBm5p0F9fV0H2q7aFl9C0IDqu3Gno8XqUNU8tVOv0EGmee7J1qhe/ITa7p7U\n9euqrl7qzAnHGjordN61TOc+1HNpoOM35z+ndB1rgY5Hx6XlqRUFAqjjI+xD7/JXh5h7wjhaLbRZ\nHaShhE42dQyphKAENwxFqJIUOgbVUaoiK20/nEMdd+i8SisoQR14oYTAGwUoqMhF7XZPjIYqSe4J\ncz8vvaAEOet+oqLrKAQ8hHmaH4IcQsCJ1tExqBNRRUER4RgSDUpQoIjaHNvpFDolZaRzF3w/+OAD\nvx/9UGd427Ztk0Igjq47deKmVtThrP2EICDtQ9vXNRd7vYfggM8//zzFTYWghLAdVQodbaEDV98P\n7SsEIKhOOA+h4z4EqsR+R8I9S53WoZNQ99HQAT1r1ixtypdErsPQsRy7D90r1bYQQJHIdRj2Gd7V\nSajzHop7Mtx/7/WdVUmkbaFTWR3JKvpuhiCXYKT5Oq+6FrVc3zm1PQQl6HeNrkF9t1V03aptugb1\nnU3kGvYrxvzIquskPaNwLAo0UAkBYbEBdro36PxoWyohKEHXvUq4n4VrRUEBKRXdT+Sm3xOhhOtR\nndszZszwy6+55pqw2N+XQue6rpWMBCXE3ktCG6MNu4kQsBE6xFVH/2ZRR7mKe+Let0fHFYruJzoG\nBbyppHT96PumOiG4QfWCje4z4dyGoIT0voOJXMfaR2xxGV98G/Tvp1DCPU7fR5UQlJDWvTysG/se\nAjV0jKm9Eg1KCJ364d9HwSml7caeh9j2aFr3Ua2j72kiJdwrFZSQ6D1Y13/s7wftJ7Q/BCXsyX0s\nkfZSBwEEEEAAAQQQQAABBBBAAIF9JTA3mwQlZNvhG9qe0cquuOR8/2p/+cXWtUtnO/mE5u7vEmYT\np07z7/xAAAEEENi7AsWLF/fpm+P34jqd42el+tn9YdhcJ5opHa7rtDDXaeXrKs2vSu7cyX8Vhc/x\nqaCVelrpszVcguucsrp16/r1Y38kUie2fux0/vz5Yz8mm3adDck+q21Koe86rcx1VCZbltEPSkuu\nIRlcJ1yyYQzCdlxHmp9UuuasLBqiwHVC+n3Hp0TWsBJKCx2Glwg24dyEdigN98aNG/1HpX5/yKXf\nnzx5sk/p7joH/HnSQqX4Vwkpw8PQCn7m3z9cp5G5TmFzHSk+lbrrsPFLXIeVf2/VqpUfwuDrr7+O\nhilI6zp0nUp+jGnXwWYydB1S5jpg/TXkOuN8+v+wf6XD1rAN7slWP5SD69RKlupb9VK7jrVMxyMv\nHV9qpVSpUr7d3333nT/PGopD/q4Dy8444wzTkCYqrkPJv+saU2p4vUKJTSuveUopHsoRRxzhJ13H\noT8OnRelpQ7nUOfYdQqG6qm+yz8U12noJ8M5Vlpwldj9uuATPy+9Hzp/up+ouI4ePxyB60Qz99Rx\ndJwlS5b0y7UfDVehYUs0dEUYgkPDfWT0e+ACHPw29V0Nnq7D1t8/dFyus9YOPfRQX0fDqrgOR3Md\nWnb77bf7IRJKlCjhl2X0h9K2a/tKox97vWt4BBV9T9Iqsa66PlynmIUU7UpNr+vDdc76YRc0bEM4\nNxoiQEXfb9fJGA2nonkuC4RP1V+nTh199EXXob4XGi4iXENakOh1qDaEIVu0XpjOzHWobei7ofu9\nhsbRcAn63uv3h0qibVPdcK3qu+kyJWiWuaee/fvChQv9sBz6nsR/dzXcgH7XuKwC/rutFXT/0zWk\noS50X9K1k9Y17HeSzo89vU7SM4rfbc2aNb2jC2wxF0Tgh6hQOnp9p3SuYku4RuNNYuvETuvepjT6\n+l0Wrj8NzaDfaRoqwnVM++o33nhjtJruS+Fe47LgRPMTmYi9l6TUxnAdt2jRwlznsh+qR8NOaMgk\nlQkTJvj3SpUqRfcEFzhhut9p+JrYEnv9VK5c2Q8Don9/uEAHX80FB5jr0Lbq1avHruan0/sOZuQ6\nDhvX90Al/DtK09q361j3Qzbpd1sowVef4+/loU78u+4zGn4h/hV+H8fXT+2zC4Lzi+L/zaDf87oO\nYl+NGzf2dfX7V9/78NJ3Q+urTYkMeRPflj39bsVvR+3a09+n8dviMwIIIIAAAggggAACCCCAAAIH\nu0DenATQpOHh9p9xE90fz3b9oUNtd88k2pC337Mf5vzsOgx2Wr58ee3Ypk3svDZnRIc22C3/7sc5\n/o9x+sN2lcoVrcPV/7KCBQr4OsuWr7ABb71rq1bv6vgoVLCAnd36NDv6yEZ++X+nTrf3P/7ULjnv\nbGt8xGHRdu99uIfVq1PL2v3rYntz2Aj7+be5Vq1yJZv9869WuFBBu7PjTXZI0SI28M1hNseND719\n+w4rUbyYNT/6SDv1pF1jD2dF+6MGMYEAAgjsBQEFFLinuaPOZ/1R+fDDD09oT+o4U0efOoHUIerS\npUedP2XLlvXbCJ2xYYPuyWQ/WbRo0TDLj+1+8skn+84zBQOEzqmogpvQ+O/p1YmtHz/tnn71HU2x\n89UZpj9G64/isUXjjGu+S3sdOzvD0+ok0Xjc6lDQNlPqANXxquMvdKhleCeprKAgERV1yKVXNHa1\nijqvYs+LzpXapqIORL1iizpFtH2XEjt2dorT6mTTSx1iGpdbY3ormKFixYq+vjox1IGmok4gdbKE\nDho/M+aHOozUcaQ6I0eO9EvUaaZObwVOqHM3HJMCQsJ0hQoV/Ljh7mnUZJ3VaV3HMbtNddJFvPog\nCO1f3x29XPYB3zmvsaz1/VBAhss24K9xbchlbthtexp/PLaE75DmBRtds6FeOK6wTtWqVcNkqu+x\n2wwdQtqmijoP3dPhkZfm1ahRI7oG9Dm1onqhqDNYxWVJMZdiPcyO3ufPnx8dQ7i+wsKUOv/CspTe\ntS0V9+S2f8XXcU8/++tWgSv6PqvTVi9953Xfix9rPH791D4rqEJFHZmxxT3x6z9qv2kVfQdCUcer\nOpXdMAt+lr53t912mw/0CnXinRSMEf99VGBKfABSaIfuzQrgCUWBCirpXYfx5yNsX9/BPb0ONRa9\nAsz0nXAZJXw7FDyhTm9dw4m2TSvqvh6K2qb7iO4tuh7ee+89v8gNGRKqRO8KOFCJP38hcMZlAPDL\n07qGfYV0fuzpdZKeUUq7dan2/X0nHFuoE/97p1q1amFRwu+6d+k+9tlnn/lgBAVQucwufn116qoo\nCCC2nHTSST5owA1lkywgJrZOStOx95KUlqvDXh3SbmgDcxl4/Ev3Lf1OOPHEE809GeBXc5mJUlo9\nWZBG7PWjyvq3h8so4YM6Crj/R8pSv1NSKul9BzNyHYfty0rHEoLNwnx9113GEh/IFealdS8PdeLf\nFTiWUvBa6dKlfZBffP3UPodrLP5aevjhh61+/foprqbf9yGAURV0T9P3Q4Gfug/onhIf5KB6us/o\nXMSfqz39bmmb+p0dyp7ex8L6vCOAAAIIIIAAAggggAACCCCAwP8Ecv9vMntPbXMdW0OGjvCNPKpx\nw6ixz7z0us384Uf/ub4LEMjnnrz5ZtIUe+vd9/28z7782mZ8P9sK5M9n/6hf14oULmTzFyyylwa8\n4Zdvck+U9Xn+FVu5arVVrFDe6tWuaS7Fur09fKQpGEFlo+uEUUDBpk27nj7zM92Pbdv/tDVr1/uP\nq9etd/U2ueCIX9wTfbltu3tSRQEJrwx802bN/skHTNSsXtVv6+PP/2O/zd31R/LMtj+0hXcEEEBg\nbwnEZ0tI6+n02DYou4ACGNRRoSfP1bke+4f00ImmP7LHltBhGTqj1Wmmjjz9AV+dSOpEji+J1Ilf\nJ/6zOrvUgRn7x2g9Qa+iDsHY4tI/+48pdWbF1ktrWh2fCkhQZ5uezgwesevoSV09Nainw0Nmidjl\nezqtY9QTyPpjf3iqNK1tqbNeJXQuhbr6Y33oYFCAgLINxJbQ5pQ6ElRP514dRaGzNayrJ8NV1PEX\nOgXjO9TjOyDCunpXh4iesr/wwgtjZ0eBHepIDNdXfKdqmB9WTO86DvXSen/00UdNTxPrfMYWdRqp\nc1QlPL2r60DHJuv4lwJXYktqrqHzz6Wdj60eBRYlmxn3IbVtqppslOUgvrjhCeJn7fY5ZNvQgtAJ\n6obm2O0YdczqSAzfh0SOQUEjsSX2Og2dyOqIjvfUZ3WMqihoRYFNeqrbDaHggzwUpBDOS+z2E5kO\nHYLx7Q9WtWvXTmQzUR3di8I1r+wLCipS9g9dE2q3ntiOLdp/CPAK8/X0sgJyYgPBvvnmG38fUraF\nt956K1T1/olchyGLRbRizMSeXoc6Z+pA13dYx+lS6vt7ZOhEzsh3JPZ3jpoWnjBXJ6e2rUwHsZkj\nQvPDNeqGFQiz/LvuWQqyCsFZaV3DyVZM5cOeXifpGcXvTm2+4IIL/O9g/S5WB7a+N8qmoSwwsSXe\nLHZZatPqFFcgj35H69yp6Dulktp3OXwXYn+/pvVd9htzP2LvJWFe7LvuYbrn6npXxh495a/fCfqu\n64l7dbCryCSle0KRIkWizcVbtGnTxi9zwxj4f9vog67PlEp638GMXMdh+/pdnNI9OGSoiA1ES+te\nHra3N94VvKjgFF0P4dwnsh8FRSlQM7y6d+/uV1PQpooyT8QXXbvKZqF/H4RAiFAnI9+t+EwMuveE\nsqf3sbA+7wgggAACCCCAAAIIIIAAAggg8D+BbBuU8NRLr1mXbo/7110PPWb3PtzTVqxcZcpi0PLv\nYRzmL1xkCxYt9vN6dr3XOrS73A3zcLv7Y1Ve+3bm9+4PT9vsR5e1QOW2G6+1a6+81B686/98toId\nO3Y99Td85Cc+g4KyF9x+8/V+G7dcf7Vf54NPRvv3jPw4vEE9U1sef7CLD3T48ZffrHgx97Tdg/fY\nzdddbbfdcI3f3ND3P7SsaH9G2kZdBBBAYE8FlC1Bf2DOSJYEPWGsJ12VPlmv+D+QqzNbf7AOT6uq\nbfoDsz6ro0hPtapD+NRTT/V/bFbK9fPPP3+3Q0ikzm4rpTBDHRb6Q3RIg64qbuxxX1NDE8QWpV5X\nZ07o8Ixdlsj0kCFDfJp4ZRJQp0lIax+/bkhrrTTUWVnUEaNMD8o4kEgJxz9q1KioutqmTgCdHxUN\nl6CU8/PmzfOf9SM85Rz71He00E3oiVU9lanrI7aEa0KdhWHf6uQIRZ2tCuRQGu+USghkiM/QEDIr\n6OnNRo0a+VXlH1vUoaZrPaS7Tu86jl03tWkFnqgoECW+A1CdgyqhPU2bNjUF2ehJcXVA6aXONV2f\n6lhPpKiTT8cQskSEdfSEb2aKglh03YwdOzbajM6L5mWkhECQ4cOHW5kyZaLjVAe5jlPv4QnbESNG\nJNt0bGBGeCo/BDKpoq4NBfKE0qBBAz+p6yV46l1PmisLgJwVZKROLT1drWN0Y5xb//79/XohkESd\n7/EdpmEfeg9p5EP69NDRqmOMLeF6Sy8YSAah6Hh0b9I6CihSoJfukeq8UwCPgkVCivxwfel60jHH\nBiaEQKQ5c+aETfun2rt27ervxQp2CJ1x++s6VPv1NLsyrKhTUZl29J3U8BshQCQzbZObgi10/vXd\n05PvKRVdI/oOxV5vqqf7gYLktFwlrWvYV4j7kRXXSSJGYT8hy8m4ceN8SzT0hwL7lJVGwSkaPiWt\n61orhW2FayvukPxHPamuc6bf+bo+9d0KnbnhOxh+n4b1Q/CCzm0i3+WwXnrvGnpFw0boO6vfSQr8\n6tSpk19Nv5+0PxVdT+GeoKAx/fsmveFhFIyi31k6Rg15oiCX1H5/p/cd3JPrWMel+23sUD665+g6\n1L0rPojCH+g+/KHrTZkjdB9RcFn8v/vSaoru/Qq0Cq8QhHTJJZf41XR+4oO8dO9WwKq+18ogEVsS\nvQdr6CX9zg3fFWVkCEPlaHvazt74fRrbVqYRQAABBBBAAAEEEEAAAQQQOFgE8mbXA83r/pCkPyYp\nQ0L4I8Fxxx5l57dtHTX5ezckg4r+GDR23MRofpHCRWyty1wwd/4Cq1Kpgg9c6PX0i1avVg1rdkxT\ne+DO26K681xgg8q5Z+16KlPT1atW8cNAbNv2p4U/Lmt+IqWFa6OK/oA3xwUkqKjdyp6gUqliBbur\n441Wxj2l8+kX//HzMtN+vwF+IIAAAntZQPepe++9N3rSPL3d6Q/mGpNYf8jV05Dq5IstHTt29B36\neldntparE+OZZ57xf8zWU7sq+qw/OGtsao3zrlco+kOyOhoSqRPWSetd6af1ZLQ6wV544QU/7rZS\n7OspyNCprPUVBKHO+EQ79OP3qY4gBXmo6I/oPXr0SFbl3HPPjTqof/rpJ78sZCNIVjETHzQmukro\nnInflDrilGZZ50VPwMpfBursUQeoOpt07hRUEjp71Jmjji91TCkzhv6wr0wQKko1r6InuvW0rkzV\nYaFgBbVBnUb6w7/2o8wJ6ghWh4Q6irRfBYCoo6JgwYK+s1qd+yoa7z0UZUXQPtU5o6EM9HSszqP2\nrU4NBSQonbfarI5cBZTcfffdPiBC7dSwEQoWUSed5uvfIIlex9quAjbUgR46K0O79K7j1HHoaXYd\nm/YlQ2WCUMey2hRS599zzz2+I1ROd9xxh9/eI4884r8HiWbm0L9B7rvvPm8sZ11TClDIbFCCzome\nXlX71ZGtf5+pbRktyqCh7ei7r6dc9X1QR5s6fBUcoEAXPQ2tYBWdC50fdUipQz/cG7RPbUfXzACX\n1l/GMlVnc2xRR53sNTyD7kcy1Dnu06ePT+WvABYtV6eUUvvrO699P/30034zIShG6yoYQ9eUgqNi\nn0hWxZAVRNeBvq/t2rXz/vp+6zuia1DfZ12PanPz5s1jm7nbtOoriECdvbfeeqtfLiedWwUz6VrT\n09oKnlGGEn1fVUKAiK4d1dF25KysJrpOdZ3p+xTb2a5j0/1ay9RWdbbur+tQnaoy19AZctS0gov0\nvQlZDjLTNnWS6joOKff1PUupqF74Dun60/1FgXHqdNe1o0wK6V3DKW03K66TRIzC0/66n+qaUaet\nigKbdF9VMI6uC5X4jl4/M+ZHyArRu3dv7xACqGKq+EmdH31vdN/UvTQUDauka07fZW3rqKOO8t9j\nZarQPTxsP73vctheeu8aEiZ8v/X7XJkFFESg+6y+LwrK0H1Lv6fUea6gv0GDBvnfX8oWIq+0irb5\n2muv+Sr6d0NqJb3v4J5cx/q9pu+6slAowERZHzTkge5f+s7s66J7xYwZM/zvgpBpRt9VZTfQ/Twr\nigJo5K1gEN3nL7vsMv/vAN3/wn0sZByK3Z/ua/r9l949WMEhurfr3qffR7pWlE0plER/n2o/uj8o\nqDIE5IRt8I4AAggggAACCCCAAAIIIIAAAkHgpJuS5s6bn+SGLMgWr8f7Pp90850PJP0+b0HUngFv\nvuvndb7/4aT1G/+I5r/w2hA/X/VTerlhEpI2b92W9MQzLyVb3rHLQ0lffDXeb0fb1Of44+/19K51\nfp07P+nD0V/49cM6oa72+Vif5/y6Tzzb39dZuXpttK3X3xjq502eNjOaF9bVe1a0P3Z7TGePa5jz\nwHnIimvg9wVLUrxvZGTb7il091Dt/iku/bkG5E31FdrmAhaSXEdgVM91GCS5sc2jRruOlGhZ/Pbc\n066+XiJ1og2mM+GeTk5yHRfRPl2HVZL7Y3+ytVzHol/uOgKSzU/0g8seEG0//pj02XXURJtynR++\nrntCNZqX6IRstT033MZuq7jOTr/MZXzYbZlmqA1a980334yWy8E9Iezna5ncXZBItFwTriMoWq46\nrpMpyT3lHdXRedd8F8AQzXMBHkmuUzjZei74IcllB4jquI6jJNdhGtXROXKdFNFyTeh6CNeEPmt9\nFzgRraP9ug7qJBcIoMW+uCeEk1xHWVTHdWIkuQ7gpOCd6HXsOtb8NtzwH2HTu71rX64TLNn1pTa5\nJ3mT3JP+yeq7TpFk9eTuOmCiOi5YxO8v1kjO2l44Zy5AI8l1yCTpmDRf766j3E+vWbPGb8sF1iS5\nzkI/7Z6q98t0fcYWrRd7vlzntj8Xmu+CapJcR6VfL7ZO7Ppqo/avNscWeYTj0HK9XGd7kuv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3QQmzZtiqbz5s1rhQsX9utqpqZD3e+++87XU6aG+KJl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\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77LpjVLIcRdM+lNu1q64ade/aJGL+/Bh35Mnx0pEnxgu7fiDq35gaNV26RJe11yyrFo+v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\nkpeAaK601EY+rhA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+ }
+ },
+ "cell_type": "markdown",
+ "id": "3c0859eb-1388-4adf-99a0-a3799c5dde9b",
+ "metadata": {},
+ "source": [
+ "### Check on JupyterLabs\n",
+ "\n",
+ "#### Status\n",
+ "\n",
+ "The status bar in the table will show you the status of your JupyterLab. If it is running, it will be green. If something went wrong during the last spawn attempt, it will be red.\n",
+ "\n",
+ "A running JupyterLab with a green status bar: \n",
+ "\n",
+ "\n",
+ "A JupyterLab which failed to start with a red status bar: \n",
+ "\n",
+ "\n",
+ "#### Logs\n",
+ "If you want to find out what happened during the spawn attempts, navigate to the logs page using the tabs on the left side of the expanded view for your JupyterLab.\n",
+ "\n",
+ "There, you can see each step of a spawn process. If you need more information for a step, for example when an error occured, click on a log message to expand it:\n",
+ "\n",
+ "You can even check the logs of previous attempts from within the last 24 hours:\n",
+ ""
+ ]
+ },
+ {
+ "attachments": {
+ "d695f798-0d79-4244-afc6-fff17aac7167.png": {
+ "image/png": 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wFVRhN9LB3I79R4tzioVsGdJI906N7SZ+P7fsPewKqtCGOohrB3p89vOfrmcm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\nkid1z3S01+t7wtf1fNjtoQj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Ponebp0qVSsqX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AACCMRHgd5vvCGZM2d2Nv3kyRPSoH59GTdurDRo2ECqVavuTCtbrpwMGDjQGY/swEcffWiCKjo8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\ns8NHDjvza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B0463keomngKMTB7QIbHJs8N32x4YYbmgiKVDY4vXEQ\nZhmiBurF0evvEVwZ06w25r3D6bnBBhuYk97HOXXR1/Rfs2bNrO8RlyBKoj7axg/7ztLvjzMToY8b\nzmGeg/I4d5l/SPuDkxybMGFC2GSTTcJXX31lYwFHOu/jNttsY+cRXNCH/MAcZ2bSGE/0K8YcQmh6\nDC44/nDEM3ZxunL/pFDMCmb8wnFKnfAlZQxj1R3gCGhYJY6jnPOsEEcYQdtcPANr5kbaQHt41xEQ\nwTptvJ+MHeYNovIgXMozDhk7zIHHHntsWGuttUyAQFuT70aecZhuD+IHhDDw9yhBOD9dOJKnbZTh\nfWMc827Biv4jWlDyPR06dKiNIeZLfwfcmUqqAb77EBshEqCfGcc4eLE8Yzj5bHU5TsoxatmypfUH\n9yYilAsaGXd8nzKeiVCEwIJ3ecCAAdbMb7/91ljwDvLOMabggjABQ5ySFdGINA7MrYiT3Bh/1O3X\nwg9REffk+wbRGvdx53ued7nUXOL39C3jhHmatiIC5J585zJWMd5HxjvzoYsumL/4LuQdwNLjh7kH\nYRZ1v/LKK1aGX8w1zFOkVfL2eQquSu9gnnFcuNGMHb4DmCsR1zGHMbfRBvr5+eeft1I+jsvN5el6\n/TPiGL4X0j/+ne3lym0RQfLvFP6t4d9H5cpXOse/Pfme83GcLs87DQf/Xk+ezzMHezq35HV8J/j3\nDMdrMo8l69O+CIiACIiACIiACIiACIiACIiACIjArCfQoBCvIt5rsfUOrpqVPx+P+aRq6rSf9SMG\nGgMaAxoDc8kY+Gjs+Fr3RXSOxoWVtbPoVKmKX0OFn+hYqYqREKpdaXQ4VEWnvNUzePBguz6KLOxz\nur640tSOx2gGM90nOharohPHzsec2jOd50CeMlkXRqdbFc+XtvgH/arowEgfroqrdK0dUegx07nq\nHogRL6yu6CAtXMpzpPlHUUPhfKWd6AS3OqMztVA0OsrsGM9Ev6633npVvh9X3RbKxXDeVdHJVsXW\njf6DUXQ+V0VRiV0fV6b66Squp84oxikcGzhwoB2LTng7xjigXtqG5R0D0clj9STHYhQbWB2lftH2\n6Mwrui6GEq+KAp/MS6Kj0spGMUjm+Ri9pVBXzBNfVIbn4bl4vlIWHUxVcEg+Q4xSUhUdflXRmVW4\nzNscneeFY9GBa9dFMYwd87ZGJ2mhTHS0WZno2LZj9Cv1ex/y3FGkYmVoC0b/R4GS7UenqJ2LaU/s\nM7+iw86ORee0HYuh1u1zXLlbKBOFPHaM8ZFl8OSZGTvRYW1FomOzKgqo7Lgf40RcWW/HouPSynEN\nzxCdrvY5OhULzxAjDVh91B2FMXbef/EeM09g0SFodfoY5Bj18dwwou+cb3TEc9osOvSromPf5hMO\nwCo6L38/mfE7OuvtPv6+cQ/qZ8z5eOeyKJiwcsn3JFlddHIX1cO5Bx980I5FJ7EVZfzx3NGBW7jU\n+4FnwaLYxsrEVfiFMj5nMQ/4u8k8Shtpa3QwFsrmGYdRLDTTPZgraRvzBJZnHBZuOmOHeY9+d4sO\nauMfo5fYoTxtox9oR4yoYtfwbsb0QHbMGXGCfmUscp53jmtilAq7hjHEGOTdxhi3tI33iHc2zxi2\nCxO/6mqcVGLkzxKjLdndo4jNno1x4cbcQP9QF0ZZnp9xj/l85mOl1NzIfMJ1ye8nH49vvfVWVRQ5\n2fkokrJ6+cW9GXdcx1hhHmC/3LucNZd4GwsVx50o9LG6opjGDlOG76fDDz/cPkdhjJ3nudyYT7h/\nFCzZoazxw/tGGeYpN2fDPON9G538drrSO5hnHPt9fBsFR9YGvjvdfI7jfcTyzOV+bXLLnMjzlfuJ\nAgO7hC3lojApWUVhP0bzsvP+7yPnlFV3sh8KFczYYR7lGt7TPOZz5VNPPWXzfJ45mPGf/H7gPt5+\nxgVWk3nMLtQvERABERABERABERABERABERABEZhLCHwcfUVzWgeAFmFWah2W7d2/ao2eG1Wt1G2t\nKkWsiH9RkYmACIiACMx5AqTEYOU9K6l9dWSlVsU/jIfoCLRV90Q7YJUlRshozKMk2IfE53RYcVYc\nswKb9BHRwWYrw/0a3+Yp42XT23S0heT55ApnjtM2VoyyUpeVorUxVlTuu+++lgokmWKD1aaEJocd\nK2JZ+U5Z0rPU1KKD0C5lJSmpQliRzapaIj9Ep6Ot0KQAKz9Zle8rQImeQNQEQnwvvvjimbdnNTJG\nFAJWRrNC/pQZK4Q9GoSHn/c0HXnGAKt6YRwdnLbKmr4nWgnRM1iRXsqiY8xyrrPqnWeLTrUQncg2\nhqJTr9RlJY8T7p37ReGBhXqPTrFCWZ4HXjxfKWvevLml6mCVP/3sEQJYacv7xCpjzFd9M8ZIM+Bp\nVDiXjhrDano3j3QSnY8WgYE+ZpW/9yFRIoj+UMl4Prfo+LRd6sIIMY8l7+vRGexEmV9EhGjatKmV\nIP0C0UqYS1gV7M/pY4v7sEKcyDSkQfE0MKxyJ5JDdcxXl/Ou+n2i09nmD56LdrDaHCMaBmkTolMu\nHHXUUYF0G6zcr4mxipz6iXDg4516PNrLSy+9VLbaJFfGR3TsBQ/3T5oDxgfvBFEFWEXvfeNRTV57\n7TV7T5KrvInMQtQLIrC4MQ55L0g94mOIc3nHIW1I3sP3azMOqYNV5Mz3pFkiwgBzFd8f1WkbZX2s\n8m56hBvmU4xIHURa4D1Jv7ukruC7hqgvHqGE7yrGEMyJblJpDNtNKvyq6TipxCh9W6J2wJG5hygC\nzCWkNuCdoq+S5mM0zSRZJrnP3ObfTz7+SPMRxTEWLYIoUxjRd9yYl3yuITpCdSw5l2S10ccx0YBI\n1xXFHZbChPRbmP/bpW3btoU5gQg3zHdEJ0lacvy0a9fOonbwHRTFGlaMKFzRKZ8ZyaPSO5j3HUu2\nh/cA839HsU9kjigOsO/05Heb86VMei7nWJYxz/A9n/5Jft9lXZc+RoQyLP3vO75HGQfJH6LxYESJ\n4L33H94NrqdNedInWSWJXzV9txJV2C7tqun3aboufRYBERABERABERABERABERABERABEZg9BBrN\nntvoLiIgAiIgAiJQTABRRJbhMBg5cmTAcVHOcP7hrPSQ/ldddVXBgdWqVSu71B3KXg+OP4zUFG6E\n+t9oo43MAYigwR1sfp5tnjLJ8ul9Uk7gLEsajij+oM4f9pMWVzTbcU8LkTxXnX0cPeSnxylCne7E\nxSGAqIL6Cd2OEVqbPPY4igjdns41nue+zhxhgvcd9RCSHecsziXOJY3n5zwpWuCBuMBDnePMJU0L\nDkucaDh9cEgjxMDZhBAHAYQ71JP1su/tKTcG4gpUY40T0NOgINygTkK5kxIhbTi9cH7hTMI5juH4\nw3GP2AMHdTJNTfr6rM84MvnB6UhKB8Lcx9XwufohCpMD6QW4/8orr2w/pChAYEBud94PuMIKJzdG\n2P20pdNtOD/K4SDF6CMvl0yhw7n27duzKWvJOt2pRZ0YDlDGQbJe0rjgXK9kyXQvnhoGcQ9CnLSR\ntsSfIV13ViqC9PXJz9SFkSKHn7QxlnHgI77hfcPxzA/vPOkD6J+aGMIQDGds0uLKa/vo71DyXHKf\nMeaG8xjH+NixY+0QcyTpcBA7uaU5ISjhuZLGu56eN7wdzM2IkNzyjsN0f3j9vIPeh8nxQv2VxiHp\nIRDJ8U6QjgpDAIKwjGvzto3rmNfdaBsOXd5dxsJdd91lp0i5kzZEE1i6/1z8g7MeKzeGrUCFXzUd\nJ5UYZd2WtCfMO/5sXsa/d/yzp8Dyz3m2zF3MY6RL4rsA4QbppjAc0xhChqSR0oLvM8RzLoZIni+1\nn5xLssogOuA7lLQ7CBH5Yd7iO4Hv0LhCwy4jhU6WJYUmyfFDWf7twfcaz4dgEJYuIEzXVekdrM44\n9rphxbO4YM6P867zbwbEaG7l5nIvk97yvZolwCN9UoxYlC5e8rOPsfRYOu200wrpYdIX833Kjxtz\nGu8HwkrmAeaUtFCDsswz9EW6r2r6blEn39luNZ3H/HptRUAEREAEREAEREAEREAEREAEREAEZh8B\n/x+9hBWzj7nuJAIiIAIikCCQ/uM9f+iOaQjMueCO3ETxol2iPOyyyy7mHIuhuc1JmfyjuDsCcRQk\nzZ2ubdq0scM4/nDA8Id6HGE41dOWp0z6mvRnHHasVOcP6r4KlmgOGE7NpMVQ4vYxyyGXLFduH+ct\nq+NxGN59991F4gNW8WNJRyefeXZWWCOAcGEEx/OaOziXW265okucddHBGR8QmyCu4Kd3795FRXC0\n8ONOCCIQJFfiszIaRw857rMszxjw1cW+cph6WEEOm5j2IiD+SY9Txgr3JRJI0hCA4ATDGZpHWMHY\nxFFI9JCkg4gIAvQRztWkAzx5r+T+mWeeGU4++WQTJhA5wQ3HFw5eHJI8J85JmBA9xceAl2XrET78\nWPJ98mNs3YHpEUr8HH1YyUrVyXWMk6zV5THVRaVq7Zm8kDtyEezElBt+uLBdeOGFzaHGgTzPgIAr\nae7I5Zg7wnGmZ4lw/DzCG94vuCNyojxCC8Q0NXnX3KmZbr+zSr+DyfZn7TMX8X5hRMFgzBCFBQFO\nt27dTOiWjHLB/V2k5vWxipwx6xE6OP7ss8/a/HPRRReFW2+9Ney2225WPO849Ggifo/ktqbjkD5B\nBIDwCCd2TDtg3yM4wh9++OFqvSPpuQGnO32LoxZhCkyTETy8/T5GEUQljTkB0YcL/8qN4eR1pfZr\nOk4qMUrfDyHkjjvuaGIzxIk9evQwgRfPzzydtDSz5LlS+zj2ESPxHe1Odd4pzOd53oWkUMXfheT3\na7l32e9dLroUZZjDmHOJosR3Ot8TfE/x7wjuiUgAg4m3zQ7M+MX845ZmsfXWW9spxqS3I2teoVCl\ndzDvO+ZtYdu6dWuL6JA8xr5HCuE7/r333rPT5eby9PV1+RlhCt+bjIcsvqXuxXd6Uhzh/YDwlPcV\nQS/jNml8//NvA0QqLoDw89V5t9IRMZIClZrOY94ObUVABERABERABERABERABERABERABGYPAfPr\nzLhVw9lzS91FBERABERABLIJ8MdxHMCs/iYkdCVRBbWw0psVx6xI5Sf9R/5OnTrZH9191TDX8Edy\nPuPsYXUx4gyc2jhqCN+fJarIU4a6KxlOF/6Y7iH1KT9s2DC7LO2ERzxAZAd3ylaqO31+8ODBJqog\nVDcRGdIRHXxFN2kjkuZhypPOh+T5Svus6oQt9SSd7ESrwFZfffWZqsCpgAMq+UNECgyRgrcRwQ1O\n1qSTbsiQIVaO0O1ZlmcMuNPNV4hTD06Q4cOHmxMm7fjivDvvaHPSPIx6UiSRPJ/eZ2Uzq3cZv0nz\nMZvlkE2W833EMxhimnQUGFYZYzjHMRxHCIVw3uJE4weHOOMTh3AegxlOLY/W4dfgpK6NsXKYcQN7\nNxxoybHkx8ttPcoBgqKWLVsWnhMnP8/J1vvIx5rXh+jBzaMjuBiL44gJCCXv1rlzZ9tlzDtPtqz4\nR5QAZ4RSvHOscucZBw4cGAYNGmTXkQ4CY2ynnb52YsYvF2N5KH4ftzxj0njfsUor9GHgxvMwN3EN\n/0FAVMF7TMQbRD4IXjzdgo8vxhPPnBRXkFaD53PHK/UTXYB3lzkewYY7FOfUOKT9RBUgPQeOUSIe\nIbJAkOQiq9q0DW7Mn/Q/795eM9KLOGvfMkZ4h5LjjXN8rxF1hPNYuTFsBVK/6mKc5GHk9/FoMwjy\nMNLIIDhDMESkIIRE5cY113hdPrY4lja+W+gzvvMZn7xb7pD2d9C/T/1a+hWjb/O8y35dpS1RkxDz\n8c4iNuLfLkcccYRdNmbMGLsfHxhPPieQzoQ0FZVSDSGo4TuBZyR9DkKd9Pe3t6/SO1iTccxzMd8m\n00Ix5zAOebezvg+9PbNjy3jj3wXMI0QKSf+7r1wbmPsRi/mPRxRBoIvRP2mhGnM3ogreayJ5JC3v\nHIxQk+9cf1eIjOFpl6hvVn2fJtuqfREQAREQAREQAREQAREQAREQAREQgdoT4G9YilhRe46qQQRE\nQAREoBYEcFDjhMFh0rhx49w18Ud/cnTjmGKFKI7KpPXr189ECWxPOukkO48jhrQX/EHeHf185o/m\nOP9YecqPG38Mx1mSp4xfU25LtABWqOPIY3UrAgHCprMa1R3jXI+QA6EHqS5qYjiz+vbta5fiCEiv\n2CcqBCvkcTYdffTRJmLBocoKWVbesqrTnc41uT+reFnhyWpiIokQvYE24JhwZy/OREJ202+sRIZ/\n0nCiYbSD1BYY/CjPsx166KG2Mp7+Zd9X/JOuhVXTMMXpQj2VxgCOKxw1XEdfcw3OQZxLOITdSBOC\nQwQHE8IfBED0IykTcMwgqiA0PA5knNF5jGgU9AOOOZwrcCCcPM52nD444zDqpW8QAbjDNVk/9TCG\nbrzxRruWdCI4HRGL4BynTR6d5IQTTjBnLs9L/1Pf6aefbu9B3ggp/COSfvYw+IwpRBa1FVbwzGec\ncYalQ4E9jijaVl0j8gb18O4zFhkz9CdOawQOiKlYDY6ghagupMvBqYYowecG7kk99AkpImAMUxzm\nScPZCHtSfTAfwRBnMhEaSAvBHMd5HGt85p3n3qSZwVxUxbUIShhTCLw8+ovfy6OJMA4QDOGwhz/v\nFvMUY3DUqFE2Hmlzz549/dLMLeURQjDeeYcwONG3CLIYa8wJpLshigPvK+YiF8YOZagHzqzmZpwy\nzhCFJQUDPBvzNedoKw7jOTUOcQzDnDQscGQfgRTvHHMBVpu24ehlHDOnYLxnWUY5f4cYf8wviPsQ\nDjB2iGhRaQxn1VsX4yQPI1/tz3zFmGF+xxBnMW8hKGJcYGlntR1M/PLoHBdccIFxcBFYoojt0j+8\nN3z/Ix50I40XY453mboQ8PEeEzFkn332KUT/qPQue32VtqQX8veb73MinyCEYJ7lfeHfNMxbfP/x\n7w2+n2666SaLBkXUFniVM+okNRXG914pq/QO1mQc873Gu873N9+DRN8gfQbzF+/M7DbmCiJo8V3A\n9zsCL95VokzUNI1S+hkQAcEbQQvzPFF1+LcH85/PY3xHp415Lc8cjMCFuZ25j+8jxgr/LnLL+33K\nXM/8gPDSRUVeh7YiIAIiIAIiIAIiIAIiIAIiIAIiIAKznkBVlFUU/qqz2HoHV83Kn4/HfFI1ddrP\n+hEDjQGNAY2BuWQMfDR2fK37Iq62j4ub54wNGDAAcWDJH29bFF1URWdmoVx0elRdddVVhUZHZ1Dh\nXLq+uOrYyuUpU6iwwk5cJV4VnS+Fe0anW1V0WBRdFZ2jdj46M4qO5/0QQ1oX6k8/E5+js8mqglEU\nmxSVjWHnq2Jqjly3gi31xRQUM5WPkSSqotOhUDf3iY62QjnawLWUy7IodrHz0dlRdDo6navoQ66l\nfvo2RlsolOGZOBdXBReOVRoDFIyCmkK9XM9PdCJVxRXUhXoYDz4mOMh9o2jDyvo10cleFcUMhWuS\nO1EUYmWT7eV8FNFURcd7UT1xRXTRc0XnoJ2PEV2SVRbtx1XhVdGRVzS+aFdcUV0VIy4UlY2OnaJy\ncYV8VXQiFcpktTVGNbA2eJ9FkUlVdCoV+pn+iM5+K+NjKIqDqqLD0+qN0Q3sHOMzaVyX7K/ooK+K\nDnirNwqDqqKz1a5LlkleD0+ekzYnDR7+HN4/UTBQFZ10hWJxJbaNXz9PW7wvorPUysXoMdbvXiaK\nduw5o5O2UE90nlZFYYa1g3K847TXOVAwOlSrooimqEwU6RTqiCKFwrko0igcT+5Ex3GhTEwZUcXY\nTh7j3rCjPaUsOvytjuS8COfosCxcEiMtFDhQJ+ejM8+ui8KRQrm4mrvQ/5SLzs4q3l0sOmetfBRi\nFMoz31EuCnDsWKVxyLjknUpadHBaHf4u5BmHyevZp1+ik97qoT38MDai2K1QtFLbmPe4jj5Im8/h\n0TlddCo6V+0af37GqNfj7WAc+RyRZwwX3WDGh+SYqOk4qcSI56Z/aDfjA4uO/KJ5JQpMbI6gTEzx\nU+XfTcwFSRs9enThuijuSZ4q2qevuRf1RQFh0Tmek3fTOfIuR0d2Fe+4W6V3udRc4tcnt+lnZT7g\nGd2YZ3gfvD18b0WxhZ8u9HvW+OF7hzmEZ0i2P4ourb7+/fsX6in3DlKo0jguVJTYidEUiuYqmCe/\ni/PO5YkqbZfvguR3aPJ8FAzYszHPYD7fOD+28IAz83qMlJO8vDDf+PxTdDLnhxhFqCqm8yr0Gffk\nO+yVV14p1BDFd3Y+CintWJ45OKZZKppP+R6IIjSrx/9dlGce4zuFNvHvHJkIiIAIiIAIiIAIiIAI\niIAIiIAIzG0E+P/qnNYBoEWYlVqHjr1PqFqj50ZVK3Vbq6oBN4r/UZ9lNvLmYy2M8Cy7gSoWAREQ\nARGoFoHxEyeHtku2rNY16cITxo8LHTp0SB+eKz+TH5yVo3NLe0ktQJoPX6k7J6GxAj3+4d/C1+dJ\nwZK3rfEfd2Hs2LG24rWun5OUMUQCqE5Y8jxjgDFC/nZWq+atOzo/C89Z09QtMCWCCWkVuDcRDWpj\nEydOtMgCjPdyzzFhwgSLWOCRMWpyT8L3s5qZdlcnLHvWvUiBQ98SAcNX3vMsRNVg9XtNolew0pn3\njQgQpcY3UWJYDU0KEVbNs/KdVeikEXHjGYliw08pI4UM9cC9FAvaQgqBrBXHXM84ZRyVWtVORBos\n2Q76gHeNd8KjvZRqY/I4dfETnZ3Jw4V9xiT8WIlfynjPo1PbxllWNJVS1yWPz6lxGJ2ixo3+KsWt\nLtqWfNasffqPcU9qkmS/etk8Y9jL+rauxkklRowRold4xKnoIA5jYjoMxlR15jGeMYo5bOyXmrOY\na4k4w6p/ImVkGe8Pcwbvcql3MM+7nFV3+hjPSqQW3o9S6TqI1sF3bKl3LF1nTT7neQdrMo7pD+ak\nrLmqJu2cV64h9QnvIymQPDJLpbbnmYPhyXcN9Zayuvw+LXUPHRcBERABERABERABERABERABERCB\nWUGAvwe1adtuVlSdu07+/tHtb+flLl/dgi1bNAstJj4fpsS/P0lYUV16Ki8CIiAC8ziB+iasmJXd\nxR/CKxlOUpyptbU89+I+pZyytb2/rheBWUmA8P6EvicNCGkzcEQRzp7w588880whbcasbMN5552X\nKayYlfdU3SIgAqUJIEyI0SAsNQNpKUgHQQoemQiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIwNxBoH4I\nK5pGYcUIE1Y0nDuwqxUiIAIiIAIiMO8RIH82q+vL/fTp06fWD4aTudw9/Fyplby1boAqEIFZTCCm\nTggxFYIJK4h+EEOmm6ji2muvnS2iiln8eKpeBESgBgSIMNGzZ8+AqCKmX5KoogYMdYkIiIAIiIAI\niIAIiIAIiIAIiIAIiIAIiEDtCBA11K2R72grAiIgAiIgAiJQPQJDhw618M7lrsoK716ufNa5Zs2a\nhXfffTfrVNGxmobjL6pEH0RgDhAgHcNtt91mYf5ZlU4KmW7duhXSDMyOJu27775h2223tZQEs+N+\nuocIiEB5AiussEJ45JFHLK1U9+7dyxfWWREQAREQAREQAREQAREQAREQAREQAREQARGYBQSSUcLr\ntbBi6s8xr/Hnk0OrZouG5ostPAtQq0oREAEREIE/MoGOHTvOlscjX3znzp1ny710ExGYkwSaNGkS\nevXqNUea0Lx588CPTAREYO4g0KhRo7DpppvOHY1RK0RABERABERABERABERABERABERABERABOo9\ngXoprLj32TfC0ZffE76d9E1hACzSYrFw1gHbhl037lE4lnfn+bc+Ch+MmxT+vtladkn3PmeHab/8\nFt4ZfFLeKlROBERABERABERABERABERABERABERABERABERABERABERABERABERABERABERgLiLg\nyUDqnbDiuH/dE66/6+nQYIFGYf9dNg7Lt2sVnh75QXjw+bdCv7MGh4YNGoSdN1otd1dN++XX0Pvw\ny8IuW/csCCtW7NAmCit+zV2HCoqACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiA\nCIiACIiACMxdBBrMaE69Ela88eE4E1Us2a5lePqfR4TFF21iGPbacu0wLkavWG3PM8MhZ94clmrV\nLKyz8rI17rEhp+xV42t1oQiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiI\ngAiIwNxDoOHc05RZ35KTrr7fbjLomN0Logq/a7soprjtzP3t480Pv2Tb5978KHTa/bRw0yMvhq5R\ndLHEJkeE9Q6+OJBKBPvhp2lhlT3Psv3bH/1v6Nbn9/1dT70+7HDi1XacX4g2djr52rDklseEJTc7\nKqyx/3kWJcMLXHTbE+HPfS8Kdz71WlhpjzPsPtz3oRfe9iJhytSfw99Ov9HqoB2cv/zupwvntSMC\nIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIlD3BOqVsGLkB+MsBUjP\nlTtmklx/1eXt+PNvf2Tbb7//KXz9xdfh6PP/HVo3Xywct+/WYdzkb8J+UTjx6vufhvkbzRc2W6uz\nlV0iCjO2XLuL7b/7yYTw9scTbB/xxVoHXBCeGvFW2HLdbmHvHdYPn074Kvz1qMvDk6+9b2U+m/R1\nGB3r63v6TWHhJguErXv1sPvuedI14Yuvv7cyR112V3jkqdfDuqt1CvvtvJEdG3jFPSbGsA/6JQIi\nIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiUCcEqqqqCvXUG2HFb9On\nh5+++SG0XbJ54eHTOw0bNAikCZnw6aTw62/TC6dbtm4RHr3o0HDUrr3CS1cdExrM1zAceskdYcH5\nG4Xz++5g5TbsvkI4+4DtCtf4zmV3PRV+/m5KGHjIDuH6/ntYmeeuOspO94t1JO3wPTcP/x10bLju\nhD1C//23tVPDXx9t2/+8+n5YpMVi4a4z9g1nxnNPXnpYaNO+Vfh88nfJKrQvAiIgAiIgAiIgAiIg\nAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiJQSwINon7Ard4IK1xM0mj++fzZM7cLLNBo\npuPb/LlLcGYtmy0S1lrtT+GDD8fNVC7rwPAoiMD22+bPhdPLtW0VmsYIFxPHTQ4///pb4fiOG65a\n2F+tU3vb/37KVNuusly78MOX31kakUtufzL89ttvYeT1/cMhO25QuEY7IiACIiACIiACIiACIiAC\nIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACtSdAxAqPWVFvhBWNYpQJolGQhqOcjfv869C+\nY+tAebdePX5P9+Gf27VsGqpiRIuvv//RD5Xcjv/y29Bw4cZF9VF4k9U72TWfx9Qibq0XX8x3Q5MF\nF7D936b/3lWDjtktrLjiMuGTjyaEswbdF3r83+mhx37nho/GTypcox0REAEREAEREAEREAEREAER\nEAEREAEREAEREAEREAEREAEREAEREAEREAEREIE6IBADVnjMiv+pB+qg3rm9inW7Lhem/zQtPD3y\ng8ym/ufl9+z8Ol06Zp73g5O/mWLpQBZftIkfKrldYvFFQ9XUn2c6/+OMY21bLV44N1/D0t3RounC\n4anLDg9v3D4wnH7ojiay+PTjz8NuA28oXK8dERABERABERABERABERABERABERABERABERABERAB\nERABERABERABERABERCBuiFQ7yJWgG2/bX9Px7HPObeEiV99X0SSzwde+G879n9/WbPo3P3Pv1n4\nPPXnX8PTr4wKHTq0tmOeV+XXX6cXyiR3ui7b1qJbDH9tdOEw6T8eeeGd0LJ1i5kiWRQKJXZ+ieW7\n9Tkr9O4/KLRuvlg4YLt1TWSxUExLMmbM54mS2hUBERABERABERABERABERABERABERABERABERAB\nERABERABERABERABERABEagtgQYxXoVHrGhU28rmpetX+1P7cPWpfcJ+p14fVtnjtLBv7/VCp2Va\nh1GffB6uHfpMqIqiiSsH7BnW7tKh6LFuGzYiLLNki9Cjc/tw1k2PWLlT9tzSyizQaD7bPvjC2+Gy\nu54Kh+ywQdG1x+y2SRhy33Nh1wHXhkuO2Cm0WmyRcMZND1tkjH77bFVUttSH+eM91u26fLjjwRGh\nf0wD0mu1TuGlUZ+En775IazR/U+lLtNxERABERABERABERABERABERABERABERABERABERABERAB\nERABERABERABERCBWhKoV8IKWG237iohRHHFoHufC9dEIUTVb9MtrcdKnZYO/XbaMGy/XreZkHaK\n5867dpgdb7jQgpaKY8t1uhTK7bj5WuGuh18Mp/9raOi9btfQsKBbCaFdq2bhrosOCXucdkPod9bg\n3+tYuHE4+aDe4cAo7MBc5fK/nRAaNvz9aMMGv28H7r1lGDPhy3DN7U/aD9d1WalDuOHEv7ErEwER\nEAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERmAUEGiy23sGeFmQWVB/C\nyJuPDW3atJkldde20t+mTw8fjJsUlmvbKjMlx4PPvx32GnCNRbnYfK2VwmeTvrayWff95oef7HCz\nRRbKOm3HJsUIE1On/RzaL9m8ZJlKJ6ZM/dkEFsu1axUaL1DvdDGV8Oi8CIhADgLjJ04ObZdsmaNk\n6SITxo+LKZE6lC6gMyIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAn9YAmPGjAlt\n2rabo883YcKE0O1v582yNrRs0TS0mDgiTPnxx1CvPfPzNWwYOrVfMhfoBedvVFJUQQXlBBV+g1bN\nFvHdGm8XbrxA6NJx7hSq1PihdKEIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI\niIAIiIAIzKUEGs6l7VKzREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAE\nREAERGCOE5CwokwXrN99+fCfq44JvXp0KlNKp0RABERABOYkgV9//bXi7X/77bfZVqbSjabHNFSz\ny+qKTVZ7qbtc/TxnVVXdZxvL05dZ7a10HW2tSXvLMUi2Y+rUqcmPM+1Xat9MF9TDA/TPp59+GiZO\nnFijvqqHyPTIIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACdURAwooyIBdZaMHQdbm2ga1M\nBERABERg7iHwzTffhCOOOCIsueSSYf755w/t27cPAwcODGnn9TXXXBPWX3/90KhRo7DmmmuGhx56\nqOgh3nvvvdC7d++w2GKLWZkuXbqEW2+9tdplii4o8eGDDz4IBx98cGjWrJm1e9999w1TpkwplN5h\nhx1Cp06dMn8om9cQNPzzn/8Myy23nLHh2f7+97+H8ePHF6qYNm1aGDBggLUDNpS97rrrCucr7Uye\nPDl07NjRmKfLjho1Kmy++eZhvvnmC8svv7zdhxxnee35558PDRo0CNdff33RJc8++2zo2bNnoZ9O\nPPHE8NNPPxWVyfpQaQyMHTs2wL5p06ahdevW4YADDggfffRRVlWFY3kYFwrHnYsvvjgstNBCRf3t\n5yu1z8tlbb///ntjtfPOO890GlZw5N5J+/bbb+34XnvtlTxccn+jjTYy7l7glVdeCXfeead/DOnz\nhRN1vMO7Sv8svfTStmUM15XBAlY///xzUZVffPFFYE7g3LXXXlt0rtSH//znP1ae7dxs6X6cm9ta\nqW3MA2efffZM83+568444wzrpx9++KFcsVl2bnbwR4h0xx13hNdee63Wz1ETxrW+qSoQAREQAREQ\nAREQAREQAREQAREQAREQAREQgbmEgC8hlbBiLukQNUMEREAERCA/AYQCl1xySdhwww3DVVddFbp1\n6xZOPfXUcMwxxxQquf/++8N+++1nooGbb745LLDAAmHLLbcMzz33nJX5+uuvTXTxxBNPBIQL1IfI\nYPfddy84jvOUKdywzA5RDbxeHN39+vUzR+2OO+5YuOpPf/pTWG211Yp+aM/7779vIoVCwQo7//jH\nP8Khhx4a2rRpE6644oqw6667Bp5/iy22KDiOYXX66aeH9dZbz/hRdp999gm33XZbhdpD+Pzzz02M\n8tlnn81UFpHCiiuuGN5++20Td2y99dZ2n/79+89UNusATs6//e1vdioZPWLkyJHW1hdeeCGccMIJ\noVevXuGss84yEURWPX6s0hhA2PLnP/853HPPPWG77bYLhx9+eBg6dKix+u6777yambZ5GPtFgwcP\nDkceeaR/LNpWal9R4YwPiy66aFhjjTXCww8/XHT2q6++CrDC0mKi//73v3Z8gw02sG2lX/Qn4gIM\n4cHqq68e6A+35Hk/Niu2l19+eUDocMEFF4RHHnkkNG7cuM5ukxVF5ssvvwx/+ctfwjvvvGPvEe9H\nHvNx69s818zuMln9OLvbUJf3u/DCCwNzTHWivjDnrb322qFhw9n/X6HZxf+ZZ54JiK4mTZpUa9w1\nYVzrm6oCERABERABERABERABERABERABERABERABEZgLCMRY16HBjHY0mgvaoyaIgAiIgAiIQG4C\nH3/8ccAhvdNOOxWEAPvvv7+JK4jUcNFFF1ldiCVwnHnkg+23397EBjiIcKY/8MAD5qhFfHDQQQfZ\nNbvssouVGTRoUPjrX/+aq0yehtMGHNo4hDfddFO7hEgbxx13nDmpEYacc845RVWxIh+hBYKL888/\nv+hcuQ88Pw734cOHm1CEsosssohFLnj55ZfDOuusY9Epll122XD77bebYxE2SyyxRLjlllsCDEoZ\nq59xMBMpIcvgjw0bNsz6g30iZrA6HDHHMsssw6GShmgiK1oEIgqMqBX0Hda5c2eLAII4BVFE2n75\n5RcTzJQbAwhOEIhQP/fGttpqq4JQx8dSuu48jInqwbhEtJFledqXdV362GabbWZj68MPPzQREefp\newwu9957r0XKWHjhhe0YEUGwvMIK3o9yVul8uWurcw5BD+P6qKOOqs5lNSqLoIr39I033jDhEf0o\nm3sJZAljKrWWeSyvWKZSXXPr+ZpwKfUsdVlXqXvouAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAjM\n7QRm/zKtuZ2I2icCIiACIjBbCBx//PGFVfXVuSHRH0gDQtSHpHXv3t0+EmmAVeasbt9jjz0KRXAs\nI8bA0c3KZoQEhxxyiKWB8EKkFuH4xIkT7VCeMn5tuS1RA3AKb7zxxoViCDcwxBZZdu6559pzkCoC\ncUIeIxUKz0xUDKJduK2yyiq2i7MfQ2iBkeIA89X/lVZvI44g7YpH/bCLE7+uvPLKsPfeexdEFZxi\nJTmr/7kOgwVpWdJRFh599FGLcnHeeedZueSv119/3fqF9BZu2267re0+/vjjtiXVCfW6CCPPGCCy\nBuZ9wT6sllpqKRPV8DlteRmzWpyxdtJJJxVEG8m68rSP8meeeaY9F8KCLHOBhEeioMxjjz1mvIhc\ngiX768knnwyIavhB3IHQCG6MBX6I6kI6FzdERzigf/zxx7DuuuvaYYRHpNjB/Dz7CHeoiygwRCuh\nPtLbILhJRnAYM2aMXcf7Rhoa2tCnT5/M1DLUi8iB8YKgh/oRxGCkOKC9vB/URbSTZMobxFWkCEIs\nRJljjz3Wriv3i1QpiFVeffXVcPXVV5s4xssjSuH+6dQKjEsi3iQNFvCCAVsfp14GIQwpaLztROHx\neYcyvEukKSJCB2V4zqeffjoXXyK/HHbYYcaWa+HH82Cl+hHHOf2EyIs29+jRY6a0SKQ38RQzvM//\n93//V8TbbpD69e6771rf0j9EPmE8+zxE0Up9yPg67bTTTFzGWOJ5ePcZQxjCHqKZYIxJj7rz5ptv\nWrQG2snzsIWlR7UgtQt9CQ+s0n0oU4kR6TKok/cDjtwznVqqFH/qJ8UO1/t7Q7qjdHoryrlxP95x\n2HIN195www12mog1pDXCeDZEfBjs+e70FDfw5DxiIizrGZjTsxjbBfolAiIgAiIgAiIgAiIgAiIg\nAiIgAiIgAiIgAn9wAvEv54UnlLCigEI7IiACIiACs5MAzl+cdDgMPW1BnvuvsMIKFpXCnbxcQ0qH\nG2+80RziLVq0KEQ96NChQ1GVOJMxX5F+2WWXmVPKC40YMcIEGaRXwHBIVirj15bbvvfee4F2J8UO\nSy+9tF2S5TDHsX3KKadYGg/SdeQ1BBJnn332TCuxhwwZYlV07drVtjjKiQyBsIToEnvuuacdTwoM\nsu6JE5NV/ETSSBtOb35wyOJkpn/Yx5mJkMNFG4gsEAGwdaM/cIrvtdde5jT148ktqVyShvMY+/TT\nT21LhA/qdYerR74oNwbswvjL28ZnnKeE6icFS5blZbzqqqsGoquQcmXBBRecqaq87aMOnovnyzIX\nmzB23ehTnPJ+zp361EE0i80339yKMlaOPvro0LJlSxs3RLhA+MLWhRCIT3BSM3b9OsYyqWUwP88+\noibaSqoW2k1qHoRQOH9dSIPjligjCAdI5YPYiTbgEEZskGWbbLKJRZJBnIRgg/cYYQrjkCgm3Adh\nBmlXEFi5k5i2IQYiMkvHjh2tLVn1+zHaT8QSngHnOMKMpCG64Fw6YgvzF2lwkkYElObNm5sggFQM\npBVBbIONGzfO2onwhlQ9Bx54oIlFSLPijn7KEG2EZ6PtiC5gWYkvwgEEXJdeeqmJWhA2wZV3kXe3\nVD/SXvqJeyBOYh4lfREcsAkTJgT6gTQzjGnGF/PKNttsY+ezfjG3MYfSt4wX5gSERp4aKE8fMvaY\nCxHFrLXWWnZfIhZ5GiUi+iC4wBDEEBXnm2++MYHUU089ZaIaRHzMH7D0CDLwhaULLSrdh/orMWLu\noE4EDT6vN2nShEsLVoo/jHgXaA8RihBJ0A+Ib0oZYxsxDH3gEY94D4jGxDsNL4yoPS48pD6+05gb\niCSEwIJ3ccCAAVY26xmYQ9OMrbB+iYAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEA9I9Conj2vHlcE\nREAERGAuI4Ajlx+cYjh3SFVRHcMBzIpbzJ1L7vhcfPHFi6ryzzg6cTwlDWfcfvvtZ4d8dW/yPPt5\nyqSv4TOrhN2x5edxsOEoTq5S93OslMdYdV5bY2U2IhaeDQct1rdv33DXXXfZam9P5YCoIRnhI+u+\nHh2BSAdp8+fAiQl/BCGjR4+2Pn3rrbcKK8lxJLPiPOlw9OckxQaRRtKGSAHnOJEQcO5ifMZwdmM4\n/9h3AUaeMbDyyivbtTfddFMhWgLOWG8Dz0nKlkqWxdhZl7o2T/sYozhOPfVIVl1EYoE1ggkMEQ/p\nTeAEYxz6OOiJBOKRFhA+8N6Q+oUIHTiq55tvPrt+5513DqR8Yaz7+8IJuOIUx6m+0UYbZUbhsAri\nL1LL3H333faR1e4rrrhiIFIGznUEUERPuO6660wMQSGc4YzJUoZTnQgQOP3dAewO51deecVES1xL\nlADEAAh7iFTh9tJLLwXEUi4W8ePpLSIvHOMYAoLaGClr4E4kAaJREFUAIQt9RdQG+h8miEswHOmI\nRnj3/X3gONE8jjzySGs75bFyfIl6wDPw/M4KgUirVq3s89ChQ2fqx08++cTGB23xe8Ac5zuiF9rv\nES9w+nu0GCK7EA2FsdKsWTNrW/IXY46xyLU+/yGyYjxTP+MJy9OHyToQHyDqQEDDOCeSCKIVIjzw\nPvz73/+2ehGFeJogxgVjHTblBGSl7oPohucpx8huGn8R6QhhVtOmTWcac1nvEc9DFAlEIvD0CEK8\nvzwDwiif9/webDnOMxHVCGMOR5zB9xsCId49orsgnEPggtgJTkR8og8wvusQAHmKIDsYf2U9Q5Kx\nl9NWBERABERABERABERABERABERABERABERABOoTgYb16WH1rCIgAiIgAnMvAVJi4IxkVXzayVOq\n1ThaccjiPMKphPMMc2d4MhIBx/0zEQmShvABxxMrqK+//vqCo7a6ZZLlk/vu7E8e8/10JALahnN1\npZVWspXGXq4mW1ZB41TF+ZZMsYFjHQci7HCi44yjLKKImpoLHHAY4xwkbQGrtnHwIYJwhzWCEsLP\ns8VYQU7/IU5IOvKT7cBhiiESwKHOyndWsWMeDQIHNvW6UzLPGCBKBs5hUg3g2EZUwGp/BC+Yt9E+\nlPhVinGJ4oXDedpHYZ6H5+L5ShlciEbgznrKuQgGwRJOXpyt/l6RMoH6EGEgWCDdANfjkPaV9kSW\nqKnh+HfDWYy5kIT7YYgY3Dz6hX+utEUggVOZlfpEz3DziBpEsXCjLz0CTTmGlGeM4nBmTPAu4PCv\nqdE2vx9OauYmBGQYKTUw3nWegx+3dPQe5+R1Ua4cX+/jtm3bFuomlQ79gDApy/w5mXO8PQgsYEu/\nEV0FcQyG+ApnPAKko446Ktx3332ZogrKIuSBvYsqOIYwB0EEKWDy9iH8knX4vketod6kId5CFEUf\nENGGZ3nwwQetiEcESZb3/XL3ycPI60EEgagCS/abn09vSZcCZyJd+PxFGRewIAzKMjjw3vLdhbgC\ngQbzLt+FWYbYizJ8FxAxiLGGwGmhhRYKaZbVfYas++mYCIiACIiACIiACIiACIiACIiACIiACIiA\nCPxRCFTNeBBFrPij9KieQwREQATmMQKIIrIMZ9zIkSMrRq7AKYmTD2EAK3OvuuqqghOL1dkYq4yT\n5ikCWDXtNn78eFuBj/MZQUOWUypPGa8va4ujNr0CHgcXzjR34vt1OF85TpqO2tjFF19sK91Jl0Cd\nvqIcJx6iCuonJDzGCm6c7eeee66tak9Gk8jbBmeOI9WjjlAPq6UJTY9z1x3cXifPyXlSO8CDFf6s\ncMdwwLPKH2EGwhAchjhyEWKwahwhDsIId2B6nb719pQbA7QPRzQpELg3KWWIdsAYJOJCJadoKcbe\nhnLbPO0rd33yHDwwnL+k3IAXTmKM6BIYzwRDxoNHa8EpyzjwFBWUS49HjlXX/Nm4DjETddK/GOk5\n6G/61q1DjDbi7fVj5baIPhg7vFdJQ5jDGPN0MJzr3LlzskjZ/X/961+WloOUHLwPCG1gRBSE6loy\nVRHXtmvXzqpgPHqaGUQvaUunQ0k/I+XL8SUqAebRd+xD4hcO9LTQi4gVGNFrPIJN4hJ7J3HeI5Bi\nvOCY54d+JQrHwQcfnCxe2Gc8cl3SeOf4QeCQtw+JaJI0rsdIW5FlHCeNBmmIuAfmaaCyyvuxcvfJ\nw8ivZzxXxzzaj48Rv9bTQPmc6Md9S/QQRIHMXUQlwhCGIfZq3769FyvaErGElCieishP+veDf67u\nM/h12oqACIiACIiACIiACIiACIiACIiACIiACIjAH40AC/0azHgoRaz4o/WunkcEREAE5hEC6WgA\nOFZx6uEU9dQepR6FKA+esoCQ8ggikvW5k3bcuHFFVbB6GWvTpo1tcVjhvMLRSWoMojukLU+Z9DXp\nzzi5cMwlUxF4ZIB0yggPY8+q65oaTk/SB+BkY5W686A+jxzBau6keWoFBBA1MXeUsxI9ac46ecz3\nEZvg+MTJ17t3b/txQQlOblIjuBHNhJXbMGQFeo8ePSxlR5cuXbxI0dafudIYwHGNs5F6cfqThgER\nAulHylk5xuWu83N52+fly21dsPLiiy+ak9WjHHAN6TFwgMOOseBRHXiHiHRBf/MsRHnA6e/8y92v\n0jmPDJNVjvFA9Iy0VSdCBo51nsmFUsm6iJzi0RU4nhYRJMum9xFoYcwJiHgYl6TCSFsy4g0r//OY\nlyP6CH3PuJswYcJMPx7VwutMzmt+rBzfFi1aWDHEaVn1Z4lEPFLMlVdemXmNRz8hsg0isxEjRpgA\ni0gHjBePkuHt8y0CkHQfIV5inCKsy9uHnqbG66205TmIaIM4BjEIc7jPa6XEGNRZ7j55GVFPdcYc\n5V0o41F/OIb5O5GeU38/GyzCD3MXqYuI+kPaE97xUqIaxsSOO+5o35V8ZyKwYywzf6SFjtV9Bm+T\ntiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiLwRyPAAkyPWCFhxR+td/U8QYcFUQAAQABJREFUIiAC\nIjCPEcDJiKAC4QGCCpx1lYwV0qzSJcICP2lHY6dOncx5iVjCDccRn3HQ45jFsUxKDJynOOpdWODl\n2eYpkyxfah/HJM4vT4NAuWHDhlnx9Mp2HOA4utyRV6rOUscHDx5sofpJF0D4+3REB1/J/OabbxZV\n4WkCslbIFxUs8YGUHLClHl8lTlGiVWBEAUgb6Qpw9iV/iEiB4Rj1Np566qnm9HTnNOeHDBnCJhCy\nPsvyjIFHH33Uxg5bN0QVRClIijr8nG8rMfZy5bZ52lfu+uQ50gcQ/eCf//ynHWZcu+EsJtUGEV3o\nF8Q2GGlvGJNHHHGErWD398LTaKQdrVzjETxIs1BTW3vtta0dw4cPL1QB/+SYKZwos0NUDsZW8jpS\nmyCS8lQRZS6veIr0MKTPwEnPXIN5pIRkBAHGS5Z5ug/O4SDnfYcxhiiIOog20Lp1a/tBcMA8wf1q\nY6TzwBA7eN3Nmzc3Idr+++9v59L96FE9eHf9GrZERGBc0U4EX8wdRLyhDwcOHBgGDRpk9TGWsgxR\nD3UmxRWkrOB6+qqu+tCfx8esp1ZBVIEYhEgQiDkwL5PV3nLH8jAqd33ynLfX3yMX1xElJ2mevqRr\n167Jw7bPcyAc6dOnjwkziN6EyIL+d6GL38ejxXhkmssvv9xEhLwniKkQ2yXFQjPdLB7wumrKL6tO\nHRMBERABERABERABERABERABERABERABERCBeYFAVZRVeMSKRvNCg9VGERABERCBPx4BnF1EiMAx\nlMwrX+lJyQt/7bXX2mpnHJY4+JLWr18/EyWwPemkk+w8zkHSXuBIdkc/n3HC4tx76qmn7MfrYVU5\nDuc8ZfyacltSVrCyG+cXkRgQCBCOnRXG7ujmeoQcCD08tUO5OrPO4STr27evnSL0/TnnnFNUjKgQ\npOnA+cZKfEQsOO3uvPPOcP/99weiWCy99NJF11Tnw4knnhiIloAzk0gizz33nLUBh7I7B1mRj9Oa\nfiNNAPyThkADox0rr7yy7cOP8jwbaTveeuutQP+y72lHWEnPamyYHn/88YF6Ko0B2MOJvsD5iKMT\n0Q7pMg4//HC7N79IlcJKdxyfeRjjUK5kedpHHaQ0oG8QnODsLmWIKUiPgjkTL+tpHPjcs2dPO0xU\nByIG4DAnXQjvIOILd76SMiJt888/vx2iPQhDstLmpK9Jf2Y1/RlnnBG23XbbgGAG7qeffnq6WMXP\nPtYY0wMGDLB3h/HAMxHNpraGiAIBzZprrmkpZ0aNGlWIhHHyySfbfUhnkRXRgnsjDCISAeMbgQJz\nDykasBNOOMHS8zBeuZ5+hQHzUW0i1VA3cyp18f5xT8bCTTfdZP166623moM8qx95FxAiwI824Gy/\n6KKLLDUKczXnEYMceOCB9r4Q0eDSSy/lliEtDrOD8RfPxrjdZZddbC4mzQnjmXkG8Vhd9aGndiL1\nB+8qbeW+fEZgRiQaF5Xw/tbEEINUYoQ4Jo9l8WfOYr7mewdejDei5zB+/J1N1k0kE7iTioV3kX0E\naohcdt99dyvq0Umuv/5663cX9iDeQWyDYIbvSCwdLcMOJn6lGTPHJedFitJ+REgIGBHNEZUJ/vR3\n//79E7VpVwREQAREQAREQAREQAREQAREQAREQAREQATmTQISVsyb/aZWi4AIiMA8T+C+++6r0TN4\nqHxWquOYTduee+5pwgpC+eMIpAw/rD7HceyRE1jdixGdgJ+kEbUBB1eeMsnrSu0j1CASAFExPJoA\nTtV//OMfRZeQBgUrld7CTpb5RbQLX8F/4YUXzlSSkPI4xBCX4CBFROAGt4svvtg/5tr6KmYvTGQE\nIkkccMABhedE1HLLLbcUUrUgKkEck4w+4dez9egjybppN4IU2keaBpy/tD8pHGHFNfUmhRqVxgDO\nSVbQI/Tw9Bk4AnFWJkPh+2p32peXMWXd/Fl868crtY9yCG14rkoryj1dA+k9PLKC3yfrHEIKIg7g\nhEdYgSHoue666wIpMRivCHDS6RGOO+44ixJDGfo2ed6fz/vQ78/WzxGJhbQMCFd4LxEfIEJwoU/y\nmnL7jDUEA4xhFyLhrOeYi3i4Pqst5epNnqM+RBuMDwQLRBDASY0YDKcyRtvvuOOOwvP5cyLycJET\n5RhntBljDHMNbXeHP8cQOHnaB6/HLpjxy49lPZOfgy/iGPqHtmHMffRzUrSR7kcENoi/EK3xQyQh\n2oYQAuMzwgzqQczixxAbeTQHO5j4haiDehHT+BhEtORzU54+5Fk94oJX7c/qWxz3zAUIdkgvdOaZ\nZxoDnp8f5gvGGpFRmPsQSfm1vs1zn0qMvC7fenuztmn+tJN0RERguuSSS+wSnstFhFl1IAgj0hNC\nHTfmLwSBGMI0H2uvvPJKQNhCWeokagpG3/CdBD9EGR06dLDj6WdIMybqRXJe5KKPP/64aK7yObmm\n32fWEP0SAREQAREQAREQAREQAREQAREQAREQAREQgTlMoEH4XwKQBoutd7CnBZklzRp587GFXPaz\n5AaqVAREQAREoFoExk+cHNou2bJa16QLTxg/ruCASZ+b2z6zqpyV2+4wmtPt+/TTT0344SuA52R7\nEGGwqhghSZ4ULHnbioNw7NixoUWLFqGunxNHIivoEUXktTxjYOLEieaATYsS8t6jNuXytK829Ve6\nljHZrFkze/5KZTnvq/4RDFXXEKbQhzjnfeU+7InawOp5HPfVMcbauHHjbJzxDLPDSIfA+Oa9SQpw\n0vfGsYy4a5lllikSoCTLTZgwwaKrEEGgro0oBLzjpdL7ZPXj1KlTA9FfmC+zBBy0kfGCoIaoBHnM\n+4h3Nivqip9nrqhpHyK+QFSBsMTnBiIaMfeTwqTUs+Rpf7pMHkbpa7I+Z/H3scUc59F7sq5NHuM5\nGY/0WdY1iNiIXuGRoRCWIOKrNH6T92A/i3G6jD6LgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAjULwL8\nnalN23Zz9KH5G2u3v503y9rQskXT0GLiiDAl+pokrJhlmFWxCIiACMydBOqbsGJW9kKefPOs/E2u\n6q9pe/Lci/ukVxrX9H66TgRmBQHSa5DahQgCRB3BGc4qelIIEGmhVFqJWdEW1SkCIiACIiACIiAC\nIiACIiACIiACIiACIiACIiACIlBzAvVBWNGi+WKh5RcvmLDif7Eras5MV4qACIiACIhAvSRAOHVW\n3Zf7IW1AbQ3nc7l7+DnSFMhEYG4msNNOO4Wdd97ZhBVELyDdCKIK0hNIVDE395zaJgIiIAIiIAIi\nIAIiIAIiIAIiIAIiIAIiIAIiIAL1j0ByMWv+ONr1j5OeWAREQAREQATKEhg6dGiYNm1a2TI1SZeQ\nrpDw+O+++2768Eyfs8Lsz1RIB0RgDhIgVcFtt90WEAGNGDHCUnh069atkKZgDjZNtxYBERABERAB\nERABERABERABERABERABERABERABERCBkgQkrCiJRidEQAREQAREoDyBjh07li9QR2cbNmwYOnfu\nXEe1qRoRmPMEmjRpEnr16jXnG6IWiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEAZAlUz\nzikVSBlIOiUCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIlA/CTSY\n8dgSVtTP/tdTi4AIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI5CAg\nYUUOSCoiAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiJQPwlIWFE/\n+11PLQIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiUIJAVVVV4YyE\nFQUU2hEBERABERABERABERABERABERABERABERABERABERABERABERABERABERABERCBEBo0aFDA\nIGFFAYV2REAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERCAEIlZ4\nzAoJKzQiREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERCBJIAas\n8JgVElYkwWhfBERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABCIB\nRazQMBABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERCBDAINY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Jz1EdgcUKK6wQiMqAg9FtypQp4cYbb7QUFC1atChEPejQoYMXsS2RBjCctzhYSRuCM9ZtxIgR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\n"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "3a7c691f-c9fd-40ae-948b-519e79825c35",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "___\n",
+ "## The spawn page\n",
+ "\n",
+ "Once you try to start a JupyterLab, a new browser tab will open and automatically redirect you to your JupyterLab once it is up and running. \n",
+ "You can check the logs of your spawn attempt or your selected configuration on this page, although no changes can be made here. \n",
+ "\n",
+ "Should your spawn fail for whatever reason, you can click the \"Retry\" button without leaving the page to reattempt. \n",
+ "\n",
+ ""
+ ]
+ }
+ ],
+ "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": "3.8.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/01-Introduction/04-JupyterLab.ipynb b/01-Introduction/04-JupyterLab.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3a4a006ae954c0535e3c97001447c2346462331f
--- /dev/null
+++ b/01-Introduction/04-JupyterLab.ipynb
@@ -0,0 +1,74 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "f66d472c-49c1-47d2-8d28-99749aa5c770.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "ca8705ef-351e-411b-b988-6658cc06a450",
+ "metadata": {},
+ "source": [
+ "\n",
+ "<h5 style=\"text-align: right\">Author: <a href=\"mailto:t.kreuzer@fz-juelich.de?subject=Jupyter-JSC%20documentation\">Tim Kreuzer</a></h5> \n",
+ "<h5><a href=\"../index.ipynb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">JupyterLab</h1> "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d510e4f8-4eaf-4261-a15e-358a6646a2ea",
+ "metadata": {
+ "tags": [],
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "# Overview"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "469fe0ed-79f3-412e-b1e9-c1dbf7f8ae99",
+ "metadata": {},
+ "source": [
+ "JupyterLab is a web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning. A modular design allows for extensions that expand and enrich functionality. (<font size=2>source: <a href=\"https://jupyter.org/\">jupyter.org</a></font>) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0a2e6d43-2060-49a5-9009-51a17d39bdd9",
+ "metadata": {},
+ "source": [
+ " - [Project Jupyter](https://jupyter.org/)\n",
+ " - [Jupyter documentation](https://jupyter.readthedocs.io/en/latest/)\n",
+ " - [Get started](https://jupyter.readthedocs.io/en/latest/content-quickstart.html)\n",
+ " - [Architecture (notebook server, kernel, etc.)](https://jupyter.readthedocs.io/en/latest/projects/architecture/content-architecture.html)\n",
+ " - [Use cases](https://jupyter.readthedocs.io/en/latest/use/use-cases/content-user.html)\n",
+ " - [JupyterLab documentation](https://jupyterlab.readthedocs.io/en/stable/)\n",
+ " - [How to use JupyterLab](https://www.youtube.com/watch?v=A5YyoCKxEOU)"
+ ]
+ }
+ ],
+ "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": "3.8.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/02-Configuration/01-Kernels&Proxies.ipynb b/02-Configuration/01-Kernels&Proxies.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..655f60207d8e5c1e92cd7fbd242c63a75719f4a1
--- /dev/null
+++ b/02-Configuration/01-Kernels&Proxies.ipynb
@@ -0,0 +1,94 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "f66d472c-49c1-47d2-8d28-99749aa5c770.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "ca8705ef-351e-411b-b988-6658cc06a450",
+ "metadata": {},
+ "source": [
+ "\n",
+ "<h5 style=\"text-align: right\">Author: <a href=\"mailto:j.goebbert@fz-juelich.de?subject=Jupyter-JSC%20documentation\">Jens Henrik Göbbert</a></h5> \n",
+ "<h5><a href=\"../index.ipynb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">Kernels & Proxies</h1> "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d510e4f8-4eaf-4261-a15e-358a6646a2ea",
+ "metadata": {
+ "tags": [],
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "# [Kernels](https://jupyter.readthedocs.io/en/latest/projects/kernels.html)\n",
+ "## Pre-installed kernel\n",
+ " - [IPython](https://ipython.org/)\n",
+ " - [Bash](https://github.com/takluyver/bash_kernel)\n",
+ " - C++17\n",
+ " - Javascript (Node.js)\n",
+ " - Julia\n",
+ " - Octave\n",
+ " - PyDeepLearning\n",
+ " - PyParaView\n",
+ " - PyQuantum\n",
+ " - R\n",
+ " - Ruby\n",
+ "\n",
+ "## HowTos\n",
+ " - [Install kernel with venv](https://docs.jupyter-jsc.fz-juelich.de/github/kreuzert/jupyter-jsc-notebooks/blob/documentation/03-HowTos/Create_JupyterKernel_general.ipynb)\n",
+ " - [Install kernel with conda](https://docs.jupyter-jsc.fz-juelich.de/github/kreuzert/jupyter-jsc-notebooks/blob/documentation/03-HowTos/Create_JupyterKernel_conda.ipynb)\n",
+ " - [Install singularity kernel](https://docs.jupyter-jsc.fz-juelich.de/github/kreuzert/jupyter-jsc-notebooks/blob/documentation/03-HowTos/install-singularity-jupyter-kernel.ipynb)\n",
+ " - [Modify kernel at runtime](https://docs.jupyter-jsc.fz-juelich.de/github/kreuzert/jupyter-jsc-notebooks/blob/documentation/03-HowTos/Modify_JupyterKernel_at_NotebookRuntime.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "84ef0169-387e-4d9e-bbf9-8ad083230e82",
+ "metadata": {},
+ "source": [
+ "---------------------"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e67d2834-a4ac-49dc-8c3b-43ffadcae2db",
+ "metadata": {},
+ "source": [
+ "# Proxies"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c77957d8-c40a-4d72-bdf4-f53aef67988a",
+ "metadata": {},
+ "source": [
+ "## Xpra Desktop"
+ ]
+ }
+ ],
+ "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": "3.8.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/02-Configuration/02-Extensions.ipynb b/02-Configuration/02-Extensions.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3910f860e41e0868d82d0586952369c614900afa
--- /dev/null
+++ b/02-Configuration/02-Extensions.ipynb
@@ -0,0 +1,442 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "53e2e4ba-a871-4f00-9c2c-b6b297f6fd82.png": {
+ "image/png": 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aU+9bEYJR/briazlTQbDh7E/P5F+fzEfTak7CbHYHw3rFVVNocIdNew/zyheL+DFlM9ZyW61t0zJzWbl9PzO+XcKfLhnBg9deSIdY99U4Zs5byle/ralxfPP+I0wY3pcrRw90q5/cohJe+d8ituw/UuPcxj2HuWLUANpFe3cC2VA27TlMZGggmq6j6Tp70jJQXTjdeUUlLNu8h4LiUqJCgygqsbJm5wHGDupZ6xilZeUs27ynWoGQYmsZU1/9nORNu7lu7BD6d2lHelYu78//g017DzP78Vtpe1KmLj0rlxXb9lFsLavhyJaW2di0L42OrSINR7aF0qgnzpjH1y0t9R3SxVPGeAzhB0GjIGgkWPdWrNJat4F0ax1VSJitxj9wXFs+eyFAecrMvcBe4GSUwQxvWV5/hEOeEp84YynEESCw1L4KJ4WIF3CmOtpW3aa+cGZbbfmsb9X4pBUSRp0c71UX2rINQkp+FwprK38XyPoEdBYhxD/1ZTPf8JQ9Zzs/r9nGU+/MpbDEO+oEZ9IhJoIJw/p4VZe02FrO10vW1pAMS8vM4aE3vmDuC1MJC3JPOKOpyS8upaTMueOUX1KKQ9OxuLgj5xS6lkk+klm/csKuCilU2lgbxdZyp04sQG5hqcdX/Yut5U6dWKiQkbOW2wmpK1X2JOV2By9//hOvffmzy8/giozcAl778me++2MDbz3yJy4d3tetleCc0yZcp6NpOsdPFLg9frnN4bJgid2hkV9U2mId2ebizW9+4/eNu/juxWmM7Nu16ul49xUJDL/vBV76bCEz/3xz8xpp4BEa7Mg+M3vJ9LGD2icezjnCT9vspBXFIIWbd5QmQ1RIdvl1B60ISjdC8XooP0QdIZ0mifIZCdMGs2zGwaaxtYEItYjKh7qU0dXOmfVTMcuS6rUsT7FVIOwAOjJTCPG77us7m+XOCwxI+IWTjqx+5qptIxGCRfqymf92s/VDQpIhhXwa6A8I3eH4FCPJC6hYofjHhz+SlV/UZGNOGN6H2NO26zyNBJZu2MnxHOcOwKrt+3n3hz948uaJXrPB4Owkt7CEFz5dwLvfJzutcuYuB45lc9fLH/LK1Ou5tZZQEIPmpaDEyq/rd3BVwiCG9OxUbYmnfUwE91yRwJyl6ygqLSPISDA762mQI/vE60uGDu3T9VmEoEOkLw+M9UXKEjYdzmbtQcnRohDsIgwv1luoP2oQBI2peDlyoGQLWLdDeaqrldowVcqvtN7T46sqYrVEYjLTyIguAEIQXMbFjwVUVrlShJxSudQq0Lc6e7uOfQLLq1QLzhovUFf1RSTP2q/GT7NL5HdAoKKqf9Ph0ea2rbnRpeSd75NZvSO1ycYMC/LnmjHereRVYi3ni19rbtNWUmaz89JnC5k4op+hj2pQjZnzljJr3lKPlGHOzC3kkTe/pFu7GK/LzBk0jBP5RRQUWxnYtb3TUKd7rkg4qatsaF2fCzTIke3SKXJxgI9PNS9VCMGgjgEM6gigkV+awYaDpezJUsgu9sMqw0C0kJmPKQJCxlW8pBVKd0P5fihLBdsxKgMIJAxVwrKf1eHZ5jW4FubM0UiY9j+knAq0VUqt64mfukAiOgLXnmxVqDl85jaXifXgZiUh6YJqR3TS9JSZj7loj7Z8xg9q/LQNJwtKTCVh2lstfhXdy2zem8bMeUubdMxhvTp7PT51T1oGf2zaU2ubwhIrD7/5BV//436iw7wrAWbQ8tF1ybw/1vP6V4s94sRWkldUwqNvfcXcF5JoHRnqsX4NPEOJtZxym52QQOdayW2jw2l7RihGsbWcJRt2EX2GlF1eUSkFdYTcGDQv9XZk//7O0vc7t4mt8z831N/Ehb2DubAq0bCQI7lZ7D5q41gB5FpVisp9KNcD0UQgzbZ6K/wgYGDFC0AvA1sa2I+DPRNsGU9yyfvzWXz32to7aj50xfS0otnHAj0R9ADR47R5pkMIcS+r/lO/QLrmoR+SftWOCEoAl44sIBH6i0gxF7Aouv60DudtqVpruZ2Z85Zw4JhLuV6vcFXCQELrmXRTXxat2kpGbt1xhSlb9vLb+p3cdNFwr9pj0PLJKy7h31/8XGvsr4/ZRKfWUYzq24U+cW3JKyxh497DrNi2j4JiK7qLEs5rdx7g+2UbmXrNOG+Zb9BAJJW7i+6vuGbnFfHXWXNqKJ/oUjZZnoFBw6iXIzvg9umhRbaSKQ7NgUmt/2Juu3Bf2oWfuSpbhkMrJS3HxpEcO8cLdPKtCkU2E2UOMzbdBwe+gB9N4uwqvuDbreJVgUmxpS/WkeEVeWAtkOQ38vXER0Yqmu1pENcBHYEiKWSKhOdZNnNltfaCzUgOCEQejoB6VZMQQm6XUqQihRXhhiSE1DcjxAEhyEUpqhmwqcl0VHYCTpfrJVQVPRCIrRJ5ACgCy4mqLpbN+k4kJH0nJP2FEH3r83nONZZt2cO8P2oUTfMqwQF+XDNmsFeTvPKKSvh6qXsqbw5N5+1vlzBxeD+jAMB5zuI1O1i7y/UGTXhwAM/fczW3XjqKgDMqoh06foLnP1nABwuWOX1vZQjPTRcNN75n5wCdWkfy3YsP0iaq+jpdRk4Bt/yrRq0kgxZEvbxRISz/Stm6M3DnwTQmDBtCv04daiTKN8gIVSEu2pe4aGe+jAaUoOlFbD9cwrF8G8fzNHJLdfJLBYVlghKbSqndRJlmwa5VOL6a8AdhabRtAFjahpquXft/jrk8Ue34pQ/6mIv13khV2E3hO0ieXr9UWE+S/Ea+Do9T8aoVfdmsd4F3GzKMtmzWPGCeu+31lFmzANeSWCtnHtbBtUDg6WMvn/E58LmTU1Ium3lNy5xlNB12h8b3yzZS0ISrB0IILh3et87KUo1lyYZdHDp+ou6GJ9maeoT5K7dw8yUj6rEmY3AuUWItdyozVklESCCzH7uVqxMGYlJrFj3o2CqSfydNpqTMuVIGwJGsXFbtSOXS4ef1/BmA75ZtJKegmLuvSHDZRlUUQgP9OJ5TiJSyxuQ3v7iUAF8fp2WW64OvxYxZVSkqdX4v3Hckk5St+7jxomHV5OKCA3xrqJ6U2xyYXcjiGbQM3P7r9L5rergJ5TaA3KJivvgtmWVRkUwcPpi4Vq28Z+FJFCHoEOlDh8i6vuAOoBgoprRcIyPfRmaRg9xijZwSnbwSKCxTKCpTKLKbsdp8sUk/HAQhFdd965bOD5H4+1Mkjz0lyFmiRUupBIMOSnYMcNgTn9XAoCGU2ez8tn6n0weutwjy82Hy2AvqbtgIbA6NRau21ivbvLTMxscLU5gy7oJ6i9gbnBvsPZLBnjTXm0a3XTqKSaP6O3ViKwkLCuC5OyaxcOUWp5JdBSVWtuxL87rs3NnAht2HmJu8oZoj69A0CkqsdGsXg6IITIpK17YxbE1NJzu/mOiw6hPgfUcyaRcT3mgt4taRoYSHBLDr0HEcmlbjb/zVkrV8tngl14/z7r3LoGlw+w5v1sxPYhLVvnXp2Sd4b/5iOrWKIXFAX7q3beORFVpP4e+jEhfjR5zrQjUnKQPKKCxzcDjbxrF8O6lZOun5CtmlfhQ7QnCYwnxM4YH/cMDTVW9TyEPXHZiA8sCzIQbV4Bxm097D7E/PatIxe3VqzdhBPbw6RnZeIfNXbKm3g7504y5W7zhAwoBudTc2OOfYeei4S6m2kEB/Hp48Hh83HKYeHVrRN64tK7fvr3FOSsne9Ew0XWJSW86zrzkY1juOd75PZtO+NAZ2bQ/AttR0jufk07Nj66rY0/EX9ObNOb/x4cLl1aTyUo9m8eVvaxk/pBcBblY8c0WQvy/XjhnCPz78gctH9Wf8kF5Axd9r24GjfLhgOdckDm70OAYtA7cc2cTbP/ItVrPucXX+4PFMDh7PJDYslDED+tC/cxxKCy4V6YpgXxN925no2+7MM1bK7KVsPSTu+795pzmyybOKHUxfyXRgulEW1aB5Wb5lX92NPMyV8QNrVMLxJFJKFq3eRnYD9XC//G01o/t39aosmEHLJPVoFnaH8yI4Q7p3pHVkmNt9XT1mEOv3HKpRIU9RBNZye5PugrRURvbtwuj+Xbn9hQ+4/8pEgvx9eXvuElqFh3LJ0D5V7QZ2a8/NlwxnxtwlHDyWzci+XcgrLOGzxavwtZiZdu2FNSSzFq7aQmZezUlJh9hIlztCN18ynN/W7WDa659z2cj+DOsVR+rRLL5aspau7aK5/6pEj35+g+bDLUe2QDl2vUmY6lQqyMjL5+vfU1i8bhOj+/ZicLcu+PmcGzMeX7NgaNeYiGn/+WncjEcnnqZtNF1nerOZZWAAVMTHrt99qEnHjAoNcrvEZkMpKLHy9ZKGC4Zs2ptGZk4BrQyJpPOO2hQuhvToiFoPDdE7Jo4mMiSQzLzqdWV8zCbGD+lda3jC+UJEcCCvP3gDr3+1mDe++QVNSsYO7MGD142nXcwpqStVUZh+11X069KOjxam8Ou6HYQE+jN2UA/uvzKRbu1P1fEJ9PdlWO/OHDyWzcKVNaXQe3RoxRWjBhDo50viwB5EnlbmLSwogA/+dgcfLFzOz6u38d0fG4gKC+aSob15dMoltDqteEuH2AgGdevgNKQhwM+HYb3j6NQ60lOXysDDuBdaIJVb6tNpfnEJC1at4+e1G+kb15FhPbvRMbbO/f2zglC/gOlA04p0ehbB6AdCUdRQdBEMDhOQh910gjVvu6r+1TQMezgGiy0GScXdRFdOYIk6SvJ0Rx3vPO8pLSunoKRptQ4vHtqbDrHevbmv3pHKulqyzuti497DbE1NNxzZ85BjJ1wLsrSKCKlXTGtESCC3TxxNjYVXUR+Bp9ORZOQWsCctw63W2flFLleXWxJxraN485Gbqmw1qSomtebubICvDzdfMpIp44aiS4lAYDIpNaSvQgP9+fSZu11KoClCYDKpCGDx63/GZKo+oYgMDeLxmy7l0esvrhhHCMyqWqMQwuj+3RjepwtmJxOSIH9fXk26/ryPgW7J1OnI9rvl39EmVR/TkM4dmsamfals2pdKaFAgAzp3ZEiPbkQGn71C5R1io4c1tw31YvC9ZtXfcpkUcqyQDJYwEPBHypNqYidvHBYd4pOKJWKlQP6hq9qPJL+z3au2JT7YVtHlDVJykUAOB0cwKKeeDCqgZdtFfNJ6KfhJ1/VFpMze4FWbzlLsmt6kDzqL2cRlI/rj5+MhZRAXLFy5pVEqDHaHxvIte7lkWJ+6GxucUxSWOBeRURTR4NhIT/kymqYz+7vf+eo315XqTseh6aRn53lmcC+jKgqqpe7QQgFuJWKaTe6tdrvqSxGiznEUIbDUMo6x4t6yqfNbpJjsl4LauBRCIL+omOTN20nesp2OMdH0jetI7w7tCQ0KrPvNLYjwoEDLI/9ZlPjGo5cmN7cttZL4SKii2Z8CbpPIaKRb5WcDBfJi4GJFU5+X8Um/SPSXWD77D4/aNiqpt6LyHJp+LVDXBp9ZwggkIxSh/EvEJ63Q0J/2uE1nOTa7g3J70y1cx4YHM2F4H6/mdmbnF/HD8s2N7ueLX9fwzO1XNDoT2uDsQtOdpy0IRLPHTEsgK6+QrLzm3QQzMDgXqNORFULxbNkSCYcysjiUkcX8lWtpGxVB747t6dmxPbFh7gffNyd+Zp97gOTmtsMVanzSlVJzzAYao4smBFwiUC4mftpMXS1+nOSPG6eTO/hes+Jn+SdC/qUqfKCeSBiloCTL+KnfSx/H7fz2Xt2lns4DIkICva7lWokQgqviB9XQW/QkEpi/YrNHHvQlZWWkZ+XSpe25Ed5kYGBgYHCKOh1ZFUZ704D07BzSs3NYvG4TgX6+dGoVS9c2rejRoR3B/i2zWkpAgO+I5rbBFUrC1PukZDZITy05CJDTVC1gmDbqzgtZ8WHD0sdHPxCmCuVHifTI90kgrhLl5rV6wtSrWDZrlyf6PJsxm9Qmc2TDgwO4eswgr45RXFrGj8s3e2SV2VpuJ/VYtuHIGhgYGJyD1BrIMuzBt4KFonRqKmOKrWVsO3CIectX8eL/vuGteT/yw4rVbN5/gLzikqYyo06iQ0PbNLcNzlDjp12LFLNoaP5BLUi4QCh+qNPfwAAAIABJREFU39N7ev2DIkc/EKYI5Xfp+UlRN0WKZBKmNdl3tKWiCMGIPp2bZKxRfbtwQQ/vXvL9R7NYutEz85Nyu4MjmYbMs4GBgcG5SK0rsscKul8aG1IuKNsDsollUiUcO5HLsRO5rNqxG7tD42h2Id3atSWuVQydYmPoEBtNaKD3tjddER4UaHn9m5Xhf75+ZMt5OiZODZSafIs6JieNQcA4EZ79Fx1ect+u6SbhyJ4L9PeSWdGKlPP18feOOt/DDCYM60tYkD95Rd5TLzCbVG6fONqrQuJSSuYsXee0klJDsNkd7D+ahabrNbKiDQwMDAzObmp1ZM2W4DGEDgc9EUo2g3U7aA3bWfYEOYVFrNqxi1U7Tq3UhAYG0CE2mo4x0cSGh9EqIoyY8DAigoK8JpchhODg0dwRwEKvDNAAFE08CLRugqGeIv7ej1n+nuvaj6ehaFl/Q4ixXrapt2Izv6LDfV4ep0XTKiKECcP78uWv7mVCN4RhveKqquR4i8y8Qn5Zu8Ojfe47kkm5zYG/r3dVFgxaDi25NLEiBBf07ESH2Ai32hdby1m2eQ/FVvfLNBsYnC/U+p+uqpaK2o5KEATFQ9BosB2rcGitu0DamsTI2sgvLiF//0G27K+uNWk2mYgJCyEmPJyo4CBCAgMIDQwgLDCA4MBAwoMCCQkIcKpx5w5SF0NoQY4scJ0bbXIQvC808YcmxDFQ8pCaA6G1UoR6IcgHgbZ19BGoCMufdHi1ztFGPtgZ9KfrbHeKEgGbdCmyEAQKZC837KlAchfxD8xk+eyaqtnnCRaziUenXMyS9bu8kg0dEujHs7dPIsjf1+N9n87SDbvYdfiYR/vMKyzBoTVenkwCeUUlZOQUVKvyFBUaRJso7yWr1nbN7ZqG3aG5LVPk0FzvrjX0ftgSCQt0nmOh6Tr5xU2ruXwmiqJw26WjuM/N6lLHTuQz/uF/u60725xous7BYyfYk5ZBfnEJkaFBtI0Ko2fH1lVqEZqus2X/EUIC/OncJqpGH3lFpew4eJQRvTujqgoOTWfFtn2Uldur2phNKrHhIXRtF1Ptu5+ZW8iW/UecVltTFEGfuLa0igihpKyctTsPVvs/9rGYCQ/yp1en1k4lt8ptDvYeyWBfeiZCCNpFhzOoW4caurRQ8X92OOME21LTsTk02seE0yeuDYF+1f+X07Ny2XX4OF3aRNOpdc1rUcnB4yfYn55F706taX1SFzs9K5cdB0/dKxVF4O9joU1UGO1jI6qut92hsXlfGh1iI4gOqyl/ejyngL1HMhgzoDsnCorZUEdxHVVVGNW3C6qqsmH3Ibq0ja7K0Tianceuw8dJHNjD6f1ka2o6IQF+NSZx1nIbG/ccJiuvCItZpW10OL06tnbrvlb7lNXke4bauQBLm4pXyDgoOwhlqVB+APSWE8MKYHc4qhLJasNsMuFjNhHg64uv2YzFYsbXYsHfx8fpl7MSW7mt5Yj6JU6NRaOuEkt7dVWOIXmWszvhMR02kPjIe6rD9qMUIr62joSU1+CGI6uo+nOAO3vQh4WQT2t2n3ms+k+VaKgEiH9wqEB/SUBd6hmqEMp0Cde4Md45y+DuHbn/qkRe/HRBrQ5LfTGbVKaMG8qYAd081qczpJT8sHwTpWWenSS7ElSvL+lZudz98kfsT8+qdn2D/X155/FbGdWvq0fGOZOwQD+X58ptdqzlNswm120qkVCrExfj5CF3thIT5jr58URBMfKkQL47pB7N4vWvfiH1aFa14z4WE3deFs+k0QPqvQMohPsyYM0tF+YuNruDGXOX8L9fV2Mtt6PrOhaTCV3qTLv2Qu6YOBofi5liaznPfzyf7u1jeen+mmswq3ek8sCrn7Lmv88SExZMUamVKX+fjZ/Fgo+lwm1RFQWTqtCpdRR/v30Sg7p3AGDFtn0kvfY5AX4+NRwpRQieuvVybr5kBKlHs5j87CxCA/2r2plUBSmhV6fW/Ovuq+nR4ZTwT1ZeIc9/soDkjbuxORw4NB1FCK5NHMwjUy6u9r9TWGJlxrylzFm6jsISK1JKfCxmenVszT/vvorenU6l2Xzz+zqe/3g+E4b35cO/3elUJrCotIzn3v+eBSu38I+7ruLB6y4E4IeUzUz/8AfCAv1RFFH1nTKpCn+6eAT3TEogLCiAEwXF/HX2HK4fN9Rpad6FK7fwwicLOPjtK6zbdZBH3/rytL+pRmGplZAAvyqnUlUV5vxzKtHhwTw+6xtuGj+MqddUPKK/WbqO5z+Zz2sP3sCtl4ys4UdN/+D7Gn/3/emZvPy/RSzdsAuLSUURAl1KbrlkJEnXjiPUxaS0kjpUgv1ruauZwbdbxQsJ9kwoT61wbO0tf9ZYid3hwO5wUGytXzyeQDZcpd3TSNGJOhK8hORJF07sKZLfyNeGPTxZsTh2Ay5LIUnoV6dNFc71lLqaSUiWNuVKl1XFlr+9VjL9IhGfNRPE/bX1JSSXydEPhJEyu+VMMpoYRQiSrhnHxr2HWbBii8f6vXR4P56/9xp8vKzFeiQrl0Wrt3m8X5tD84gzu3pHqsuwh49+SvGaIxsR4lpvu6i0jPxiK8EBdTuyJdZyjmS5Du0PD276nANvEV7LNVu1IxVN190Wul+wcguzvnNe0DG/2MqE4X3xacGhDE3Fut0HeemzhTx16+VcN3YIsREhZOUW8t2yjTz93jw6xEZy6fC+6LqkyFpGoYs4eGu5jRP5xThOFnnRdcmJ/GJemzaFcYN7AhWT3tRj2bz82UJuff591v73Wfx9LdjsGr4+Zl6ZOpmuZyiVCCFoG12xc2K3a+QUFPPeE7dVtdN0nT1pGfzt3bk8Pusb5vxrKr4WM+U2O4++9RUb9xzm73dOYuzAHpSUlZOydR9/e2cu2flFvP/kHRW2SskHC5bz1pzfuPuKeG4cPwxfi5l96Vk8OXsOtz3/Pstn/a2qmExxaTl5RaV898dG/u+BybSLDudM0rPz+HXdDvKKSqr5KqVlNlqFh/DmIzcSGVIxcSsssZKybR8vfLoAXUqevHkimqZRUGyl0EVxmRJrOblFFYuRYwf1YM6/pladW70jlZc+/4nn77mGPnEVDrjJpNK5TRQFxVaKSsrIP63fotIycgtLeOnTBYzu25UubaOrjZVfXFotj8Pu0Ljt+Q/IKy7l+buvZuzgntjsdn5atY3nP5mPXdN47o5JtU4Ua/3PE+huPrUEmGMrXoGjQFrBdhTKj4AtHRyZTZ8s5mWkFE2jdeQGqi5iZB3lDjSTXOVWZ2vezGT01E8Q4uFaWgWQ+EgoyW+4rAGp6MoUkLUHJEp2S3+/y1n+ah3L+dN1PXbyNDUjepCEobU0tKgoV2vwYe39ndtEhwXz6TP3MOXvs1m6cRdaI1ZmTapK4qAevPnwjV6X99J1yZzf17foOMDAWpLccgqKKbPZXRZeyCsupbTM+Wfz87HgZ3H979K9fSssJhM2R005srTMXHYfPk77mJoPwDNJz85lby3b06evQJ3tDO3ZiQBfH0qcXPO1Ow6wJy2j2spYbXy3bKPLc9FhQWfNiqm3Wbx2B51aR/Gni4dXbWG3iQrjgavH8tWStazcvp9Lh/dtcP/tYsLp2/lUtFm/Lu2ICQvmir++yaod+7lwcEX8vtmk0qVtTLW2rujSJrpauwFd26MoCk/M+oZtqelc0LMTyZv2MH/FZj566k6uGTO4yqmKax2Nrkuefm8ea3YeYFivOAqKS/n3Fz9z64SRPHfnlVVlbzu3iSY8yJ8bp7/Luz/8wcOTx1f10yYqjDZRYXy0MIW/3zGpho3zV2wmrk2U06e8n4+Znh1aExsRUnVseJ/O7DuSyeeLV/HkzRPrvrCn4WsxV7sex07kYzGpxLWJcut6ArSPiSDAz4eXP1vIzMduqXWS923yeo5k5fLxM3cxdlDPqhW5+65KRJeSp9+dywNXJRITHuKyj1oDogQNfAAKP/DpAsFjIfIWiP0zRN4KIReDf/8Kh1ecRUkXqj/4xkHgSAi/GqKnIiKuH9ncZp1G3csKDrPbF1yX4r/UXgiskOzQWoPMpJQ1/xvPHEeIu/mlLif2JHPmaELIZ+tqJhXOrhLCXiIsyJ+PnrqTmy8e4Xbs5Jn4Wsz86eLhvP/X2+nYKrLuNzSSEwVFzE/Z7DS2rbF4qk9nqyWVHMnO40S+62TYXYeOuSy32y46vNYqaYH+vvTs6NzJzCsq4btlG9D12j+jBJI37uHAsWyn5yOCA+nZsSnyRZuG7u1jXTr3peU23vk+GWt53SEse49ksP3AUafnhBB0axtjqGGcJMjPl9KycmxnlMtWFYX3nriN2yaM8viYUWFBRIYGkZ7luY24dtFh+JhNVbkGW/YfIa51NKP7d6u2MihExQpmjw6xVWEn+45kUlBiZfLYIVVObCX9u7and6fWbNp7uFpsbkRwAJcM7c2S9Ttr5DfkF5eyIGULE4f3w9fi3qq/qigM7t6R4zn5TVq6vBI/HwtP3XI5v23Yyc9rtrl0JjRdZ/WOVHp2bM0FPTpV21ZWhGDCsD50bBXJjkO150zUelWklPbazruPcmrF9nS0ItBywZELjjzQi8BRWHFcFrtVU9UjCD8wh4IpDNQwUE/+bIoAcytQnWxRFa9vMUvMmpDZSh3XShHaRB3ecavDFTN3iPip9+mIa0FW/08Uwqoi3tB3THf9BEicbhJadq0OpYCVLJ+xwi17TuJYFv2bEp99AnDpVQld9qxPn+cybaLCmP3YLQzrFcfrXy1m/xnxfa6wmE0M7NaehydfxFXxA6u2wLzNxr2HWb0j1St9OzQdT/iyIYH++FrMlNlq3ho3703j66Xr+MsNl9Q4dzQ7j9nfJbt0NvvUsTJoUhUuHNyTLfuPOD3/0cIULhvZn8tG9HO6BSeBbalHePEz17HTg7p3IOIcCi3o1DqKnh1bs+uwc4GVDxYsJ651FI9OudhlHzmFxTwxaw65BcVOz0cEB3Dx0D615lOcT4wf0ot/fvQjL322kD9PuaRaIlcvL02SSstsFBSXOk1iaihpmbnY7I6qPrcfPEqH2HD8ndwLO7aKZOlbT1T9vjc9k8iQQKdhOj5mE53bRLN+9yGKrOVVoVqqojB53AV8sGA5G/YcrrZqvW7XQfKKSrh8VH8+WLDM7c+QlVdIgJ9PgxcyGstVCYNYsW0f//zwR0b26eJ0R6+0zEZaRg5RoUFOE1o7t4lm88f/qHOsWh1ZXeLdPT41qOJl6eBsdNCKQS8DWQa2Uog4AXop6CdXNfRyQAOpgTxpqrCAOO1jKX6AAop/xc+K/6mfVT9Qg0E0QBNT6i2nSLZORt0lEORLxE/b5q7zqC2f9V/gv87O1V1r6UQXoNYnopTMdceO6kzXpZy2TAjpMqFLCuHe3sd5gp+PhfuvSuSykf35Ze12vl+2kd1pxykoKaOs3I5D0/C1mPHzsRAVGkTXdjFMGj2Ai4f2JiYs2GsSdmeiaTqfL17lkUpezmgTFYrF3PgbenhwABdd0Jv5KzbXOKfpOi9//hNBfr5cNrIfUWFBFVXF0rN44dMF7Elz7lCZTSpjBnavdVxFCCaO6McnP68kx4lTVW53cPdLH/HolIu5JnEw4cEBBPr5Yi23kVNQzMrt+/nHhz9y1MWqlcWkMmF430bpAxeWWNm87wj+PvWJoxbERoQQG+7575qqKDx03XgWrNjiNCTDWm7jHx/+wJ60DO6/KpF20eGEBPpTZrNTbC1j58FjvPJFRQKKqzlQr05t6N+1nUftPpvp27ktLz9wHW9/+xsLVmxh/JBexPfvxpAeHenVsbVHHX5N18kpKOaTRSvQNJ2ep4XFnMgv4u/vf0f4aWW0TarK07ddXiNb/kh2HoEnnShN00k9lsXLny2kZ8fWVaEndrsDk6q69R0tKLZiUhWX4Sb+vj5omo48Y1Lbp1MbLujRiXl/bKhyZKWUfLN0HV3bxdC7k3sTAYemsT89iy9/W8PlI70l3143qqLw0OSL+G39Tt7+dgnP3H45FlN1l1NKiUPTqznbJWXlZOQUoJ12fdpGhdUqnVirI2uxrd+AFtMDtTkyWZUKJ7NybBVoMVGpILC7t7zVFKTM3Ef8tOMgawtwC1WQS0lIekdXlH+T/Ha6t8xR7XSSdey06UJJaUjfUhHPK1K2k+BMgNGOu6vO5xFCCNrHhHP3FQnccdlo9qZlkJVfRFFpGTa7g0A/H4ID/E9KswQ1yzbp3iMZLNu812v9t4oIqXETbQj+vj5cOKQni1ZvcyrndSK/iKmvfcagBR3o1CqSwhIrOw8dI62WymKxESGM6NOlzrFH9etK/y7tWLrBecWzzLxCnn3/Oz5cuJy20eFEBAdQWFLG4YwcUo9l1brF2C4mnBsuvKBRf/utqUe44ok36uWQCgQ9O7bi87/f67aman0Y2K09o/p14feNu52eLyix8u4PySxctZWubaOJjQipSlbZceCoy1AQqJiA3H9VYp0Z1ecTJlUh6ZpxJPTvxi9rt/PT6m3M+X0dwf5+3H1FPI9OuZiQRlyvGXOX8GNKxSTS7tA4kpXLnsPHeeymCdXCn6SsKHN9ujPp52NG02vuRjz8xhf4nVwZLXc4yMkvZszA7vzz7qu8pjvtbGIkhODqMYP450c/UmwtJ9DPB6vNzs9rtvNq0vUuExMPHMvm4Te/qNo5s9kdbNl/BIvZxH1XJnrFfnfpEBvBI5Mv4p8f/8hlI/sxrFdctfPOrsOanQd48PX/ndw5kugS3nj4Ri4b4TrHvHZH1nFgMyc+/xPhV4K5RVZlbTYcellL0iuVSP0nhLirjnYWJA8pmj5NxCelSOQcHcdcd4sbuI3Qo+qskqvZDzWo72Vvb9JqT/gyqAVVUejZsTUtKf5C03Xen7+Mo9neEZtQFEHnNtEe2WITwB0TR/PJohVs2pvmtI2m66zbdZB1uw46PX8m904aw+DuznalquNrMfPs7ZPYsv+I01VZqHi47z2Syd4jmW6NDRUatU/8aSKtIxuvg9uQqnIpW/eRsnWfVxzZ4AA/Xrr/OiY/M6tWtYb0rFzSazl/JgK4LnEIE4fXLeByPtK3c1v6dm7LX26cQF5RCZ8sWsG/Pp6PzaHx4n3XYlIVzKqKputOZdAqw1/OlM+qWBWt+NnHYiJxYA9ef/AGhvToWK1dVFgQrz90I/3cSE56ePJFdGxV8d37+KcUDh3P4fO/31vNiTWZVIqt5ehOHOFKe0+X8KqNMpsdi1l1ujqdOLAHM+cu5YtfV3PvpDHM+2MDESEBjOzreqJbKbtVeV3CggN48Lrx3HTRMLeUTCrs17yiIa0IwV1XJLBozTYeefNLlrz5eLXzzryEQd06MPMvN6PpOtl5RTz93jyX97tKanVkNYey06wWw4kvIGgEBA6v6y3nDz6hK5vbhNPRhXxLQdyOO4lfoEhIAJGgYH6b+KSdCObrulxAyqwVNDI6WSr41dlDuX7eSmQZVOfYiXzm/bHRY1qvZ2Ixmapl9DaW4AA/nr71Cu5++aNGCesLIRjdryu31iMBZmTfzjx+06U88948jxR4UITgpouGc9NFzZsjafNSSAnABT07Mf3OK3n4zS88ooghBPTt3I6X7r/ObUfhfKCotIz3fviDYb3jGH2aDF1YUAAPTb6IPWkZfL98Ey/cew1+PmZCA/3JLSzBarPXiD3NyMnHx2IiIqT6Nuz9VyVy5eiTkukCj+wejRnQjX5dKsJDggP8mPzMLH5Yvokbxg+tcrBbR4axPz2zRhIbVNy/Hp/5DdeNHcLVCYPo2aE1mXmFTqXFdF0nI6eA8KAAp5n87aLDGdyjIz+t2soVI/vz4/JNTBzej1YRLtUw6RgbyWvTphBbmdXv5LoE+PkQERLI0ew8pxOHnMISrxZ0+csNE7jjxQ94f/6yaiEDfj4WWkWGcjwnn9IyG/6+FkID/Ukc2AOA/elZbj0Xav0W6MK2veInCUUrIesDKNtN02VhtVC0Yo05Q5wvxzQXy2dvRfBJA9/dC8lfFSGWK/FJh5SEpJdJmNrwRTu9TmdaZ8N7HkokNDib0XWdd39I5lDGCa+N4etjJq6WijkN4fKR/fjXPVc3qo920eHM+PPNtIt2/wFiMZn4yw0Xc9ulIxutW6oIwbjBPXnuzkk1qg2dSyhCcNPFw3ll6vUeWZUf0LUDs/9yCx1iI2pVmjgf+Wn1Vj5ZtKJGrLsiRFXilKZLTKpKn7i2bN6bRmZu9XQTu6aRsnU/PTu2drpKqKpKxcsLIVBDe3Zi4oh+zP7+dzJOs2t47zh2HDxGanrNiMI1Ow/w+6bdBAdU/A91bx+LlJJf1m6v0Tb1WDZb9h+hf9f2+PvWjEdXFMGdl41m2eY9LNmwizU7D3DTRcPqXC1VFaXW6xIS6E/39rGs33OoxvUuK7ezescBBnWre1eooQzrFcd9Vyby2lc/VysqYjap9Ilrw44DR50mI6/ffZBjJ1yqfFZR69XZ8en0NCk51YtWAHk/QvYHYN3aIkrUNgfSnlV7ubBmQlfkwwKxoZHdtEfyV0WKHSI+aRFjkkZ4xDgDAyds2pfGF7+s9uoYgX6+dGkTXXfDeuBjMXP/VWOZ+Zeb673aqyoK44f04tvnp9Kvc9t6JzmZVJUZf76Z/5s6mZjwhuUvhAcH8PD1FzHn+am1rvacK/hazNx35Rh+ePkht8I4XPVxzZjBzHsxiRG1bPWerwT5+3LzJSP4eslaPlq4nIzcAqAiEXHdroN8+/t6JgzrU+WUPXB1IiVl5Tz3/nfsTjuOzeEgK6+Q935IZuW2fTx83fgG26JpOrmFxWTnF1V7ncgvqjWh1M/HwuM3TWDjnsPMTV5fdfyiC3oxvHdnHnrzfyzdsIuCEiv5xaWs2LaPf338I6P6dmFU34pV6MjQQP485RLenruE//2ymoISKza7g92Hj/O32d9iszu454oEl4lvPTu2ZkDXDjzz33n06NDKba3j2lCE4KHrxrMtNZ03vvmVA8eyKbc7SM/KZfqHP3A44wT3ThrT6HFcjq8I7ro8nm7tYjl2RgjZ3Zcn4OtjJum1z9i8L40Sazl5RSX8kLKJv3/wfZ2yguBGnIAu9dWqUCZUO+jIhfyfQfxWoRfr17NCZ9WtXe2zH0Uv2tz0ymxukDyrWEucermqiR/qKBzgDkLABKFzCQnTPtMV08O1FUAwMKgvdofGjLlLOZzh3XnhqL5dPCrNU4lJVbh3UiK9Orbh9a9+JnnTHoqt5S41a80mlTZRYVyXOIRHrr+oUVt5vhYzSddcyLBecbz02UKWbdlLcWlZrWWJVVUh2N+PvnFtePLmiSQM6F5vlYJAPx9CA/0bFVJxJkH+vi71eWPDg50WNaisc18f+xVF4dLhfenWLoaPfkrhvz8uo7DE6lRK7dR7BH4WC33i2jD1mnFcPrIfYcGBdYvEnKRb+1jU1dtqJBn5Wsz1igkOCfBzWd0tIiSQqFrK8TYlU8YNZfO+NF753yI+W7yKsEB/bA6NYyfyaR8bwf2nJR+FBvoz6y+38NS7c7n+2dlEhwZRWm7n+Il8pl47jouH9qlqK4TA12J2axXWpCrkFZXw6Ftf1UjWEicduuvHXYCiKPhazDUcyt6d2jB53BA+/imFGy4cSmRoEIF+vvzfA9fxyFtfctfLH9EmKgwBZOUV0SeuDdPvvLKqEIqqKDw8eTzZ+UU8/d5c3vsxGbPJRGZuAYF+vrz+0A3V/vfNJhXLabsr/r4WrowfwHPvf8+V8QOrTXR9LGbU0+w1mxTMJtWtnYGu7WJ4/aEbeOXzRSxeu53w4ACKS8s4nlPA4zdNYPAZccaVKIqCxWRyeu0FYDGr1fRyLSbV6c5HcIAfT992OTsOHsNkOtWXv6+FGY/ezGMzvuamf7xHVGgQDk3jeE4Bw3vFERLgV+eKdJ0fv//tL/zVYjK9XFc7hE+FM+vTAcztweTZWb5dg63OJRSbHL045S6+i/dY9Sg1furtEvFRrWMGKr4setu9AK9LH/RRSvRXkEzFc0HNe3TJJFJm1plaroyeOhUhZtbSRNeXzzw/Zj0eQkq5Chje3HZ4Cl1KvvhlNfe98gmlbojSNxRVUXj/yTu4faLnhdjPZPHa7azankrK1n0cPJZNsbUcPx8zEcGBdG0XQ8KA7kwY1sfjYQ7ldgdb9x9h1fZUVu9IJfVoFln5RZRYywnw8yE8KIBWkaGMGdCNkX26MKh7hwZrA5fbHcxP2czm/Wm1Os31oVfH1kweO8SpTbqu8+OKzazZeaCaDrBJVbjlkpF0bx9b4z3ukno0izU7D7Bi6362HUgnI6eAYmsZQggiQgKJDAlkeO/ODOsVx9hBPRoUD3s8p4Avfl1N9hmFMjq1iuSuy+PdLpELsHlfGnN+X1/NKVaEIL5/t0ZVy8orKmXUAy/y8OTxHstyX7frIDsPHWN/ehYBfj7069yWcYN7Oq16l5aZQ/KmPRw8lk1ESCBDenTigp4dqzlOdofGnN/Xk9C/K21rKUoCFZrNP63e5rSioSIECQO60aNDq4pCAyu2cMXoAYSc8bdNy8xl2eY9JAzoRvuYUxOOotIyflu3k20H0vExm+hby+eyOzTW7z7Emp0HKCoto11MOBOG9TkVy3qSXYeOk3o0i8tHnZLKyikoZumGXcT371Ztx+eH5ZtoHxPBwG7tgQq1lz1pGVx0QW+XFQXPZPuBdJZv3UdWbiGxESGMHdSDrm1jXO4MZeQWsHLbfsYO6klYUHXFCZvdweK12+nWLobu7VtV2bR1fzrXjR3itL+Fq7YSFuTPyDOUWrLyClm5bT9bUtNRTuYPDO8dx7LNe+nePrbWojx1OrK9b58+wN/kt6mudjWo1If1aVtRCMEUSR2RDLXSYhxZrUTq9p98mXO9x56+HndkK0m8v4/Q1OcFXEFjLv4p0nVdDGPFjFrLbBiOrOc51xzZPWkZXPnkW+yppVyqJ+gUf3JeAAAgAElEQVTYKpL5//dwVY1wbyOlpKi0jDKbHU3XUUTFiomvj9mpmLqnsZbbKLPZsTk0dF1HURRMqoqPSSXAz8djWq1S1lUU230E1GmXs4QPT5WFtTs0SsrKsdm1qqx008lVpQBfH49kc59pvxDC7VXd03F23RvaVyXecGQrkRK344ilBIQbTkkLoD6fq6J9zQSr5qbye9SyrGqYXXWu1u34ePrmIXe9vFcI0a1e1mhFYN1e8YKKIgWV1b0ssScrZnkvS85bSNux/XzrOSfWqyS/s13CVXLkg50VVb8LuBHo2Ige26qKnKcxfSRMbzGVzQzOLgpLy3jps4Ved2IBurdzXabUGwghCA7wa7Zsdj8fS5NUYmus81RfPOW0OsNsUr2uBesp+5v6ujeW+nzsFubn1Up9bW1pTiy0PAe2kobY5da286XDLtjv72PutnLHbjJyG6iaJB1gS694lZw8pviedGxbg6kVWNpUHGvBCC1n9lmn2bDy7VQdngKeZtS0wYoirwAuBwZSz++NhGFqQtaN2jL+5w1TDc5tHJrOq18s4qsla5tkvAuH9DQkkgwMDAzOYdxyZLPy8p67bsyoiUN7dic9+wRrd+9l8/4Djdf+08ug/FDFqxI1FCxtK5xanw4Vv7cUtAJNzyt7u7nNaASSFTPW67AeeI7EB9squjYFxM1IBrjdiRTPQi2OrFD0OiTaxMnXWTcnMGg4Dk3j6yXreHPOb5TXkmDjKaJCg5hyYfPqoxoYGBgYeBe3gn/e/et16/ccOZ4J0DYqkmviR/LMzVO4YVw8PTu0xeRJPTctvyIcoWAxZL1X8SpYUuHsyubVChBlqT+TPNZ7yt1NTfLb6fqyWa/py2YO1FWtL1J+ALhzkbsz6gGXxeEFek0l6DObJD7iOYV6g7OCn1dv4/GZX1NYS9lPT6EIwY3jh9EmqgVNhA0MDAwMPI7bGe3pWXlP9uzQpiohyWI2M6BLZwZ06UyZzcaOQ2lsST1I6tFj1So3NBotH/6/vfMOj6pK//jnnDsz6b0SWui995KAoCAqdlEBy6q7KsG+uq7+1o2r7qpY11BkLaioICIKSEchCSAgAhGkKi1CKgnpU+45vz8GAiEVCBB1Ps8zj3LvueecuXMn8573vO/3LdkErg1wKA98OrpfXrFgrV9tyBpRdm0a9nsrHBv1gJco1J8Ioa8CTg9M+1kJfTvJUxpUBbBqWTVtm4J7iJv4vkR/BdSoDSOFHKlgV5UntThWa8CCyxkJnLmc1+D7Q6SQDyCoMqhNCr3UtXrKt2fcr4fzhtaaT1es5+E3P62UvX2+CA8O4Obhfc+LaLoHDx48eGg4VDRkExMly7K6WqxGgMsid7PyzfKC3S8mDJ8R/OGa5/u0b10p/dfbZqNX29b0atsau9PBnvTD7DyUzs4D6RSV1uacOwNUCRR/734BGL5gi3WHIHg1B0s0WEJAnGFCvHa6tXFdR91FH1x5YBaBOl4BQ9sB7yksvfvXUy8zivW1Wujrq+m1ldDiBQ2XnNlkzhwjLuEaBX8BfbpoXqkW+jlWT9lY585SktbI+AljlRZLa2wnaVXdKVPK/VLXnAtmSN3HhFqlvCoNK+SzwAPVBSUoLe4DPG64BoLd6WLhmi0X1IgVQjCyb6dqdRE9ePDgwcPvh4qG7PKDfhbpG4ypwelsBGSeevqTb1ffsOPA/rXXxg2Ugb5VZ3l6WW10bhFL5xaxoDXpObns/fUwPx/J4EBGVv3W1DZLoPQn96sc6TZmLeHHjVoLSB/3f7V2G626zG0Uu46C8yioWn9gDytV+vdKRxW+NXkehSa6jr7p2gXg/DOqvnHDE8K0g9kCvCrlbWkQWnRS0JoziEd1JU9ZJuMSdgDVl6nVVO8O9/baTWmpixo8/ho5gpribKtBCAbUXHpZ1J9Su4dzwmWaJM1dyQsfLCCv8MJ9LM0iQ3l4zIhzLuHqwYMHDx4aPhX/0q9pVuwanJtnMQhEqUpaoWnvP7XeuPOFGb8czrzr8n696NuhLVLUsHUnBE0iwmkSEc7Q7l1RWnEoK5t9hzM5mJ1NelYOBSX1HS+nwJXrftUTQohHWfNeZWtXk1HjFrogBhJlbVJVGtm0FjuzhDlzqo5dLZXBGKqm8jYtGTqxHauSdtY0QOU5sUfUYMhqqF47bdkrxcQlpAE9qx9Aj6HfQ0+w/s3MatucztCHg7XprFH5W2tducC1hwuK0pq96Vm88OECPv/m+/Na8OB0pBDcf/0ldG3d5LyPVWp3UFJW9Xvz8/Gqs0D5haSwxL1DFuDbsNVhPPx2UUrz/c59zEv5gR37jyCA3u1bMG5E/wqi9i7TJOHVmZWqtp1gUJc23H/dJRwrLuXNz5YTGx3GLZf2q1AFC+BARi6vzVrKC/fegFKK12YvY296zT8rN17Sh2vjerBoXRqfrtiAPr6DKIQkPMiPLq2aMLRH+/LiJUpr5q3exJcpm6us3Ofr5cXfxo+iVT2XwvZQN05zWSQqUtlak89U43ys1GGM/jL1u4i123dwRb/etG/WtE6DSSFpHhVF86io8mMFxcWkZ+eSnp1DVv4xMo7mcbSgsEoB7AuNISU+3l6pBUtf+ayq86YhMmTN8wxkcNZAUkmtqZGAK2p5t/urPaMth6FmQ0GaerSCMzJkJSKoRslzQc0uNqEXo0X1hix4S6vrNQXj6jwn5bjPXUKuhmEFGy/+k/PHxeF0seL7n3huxgI27thXqTTn+aZLqyb8efSQM6qYdDaU2Z08N2MBKVurjo65cmA3nhx/xXmdw5migYff/JSsvAIWvPzQxZ7ObxaXaSIQGPVQKOH3htKaL1Zv4h//mwcCLu3VgRK7gxmL17DouzSSHh1PjzbuqlSm0kyfv5r47u1oFFY59/dYkdvJVVRSxvzUzfyak0+TyFCG9aroXzmck8/0+auZcP0wokIDsTucFarOLVy7ldaNo2jf/GQVuBMJp+u2/8yS9T8S360tVouBUiZb96bz4ZK1dIyN4e0n7qBjbAxaazbs2MeS9dsY1rNDJR1ZlzJpACbLH5Yz3nvbMiMxv9sd/5pos3rPyso7JmYsWUnLRtFc3rcnzaLOfDUS6OdHRz8/OsY2Kz/mUibZecfILSggv6iY3IJCsvMLOFbk5FhxMQXFpahaYjDrghCCID9fgv39CAkIIMTfj0ZhIcSEhRETEUqgr69evmnL058srcaiM4v3In0cVE70KscQ4gVzaOJwViVWuT4wBk+4VaN71ThRzZZqz617vZS4hN1A9QUrNI8x+P53SJ1aNxHgAY/4aBw1zkkoDtf0vVXI2RL9dI3jCMbKwQlpKnXyS7XOadDE3mj9f7U1U0LMqrUvD+eFvelZ/Gfm13y6fD2lF9ALe4KokEBef/BWQgP9zvtYLtNk695DbNixj7ZNoyuFMdid519e7EyxO5ys3rKLrLyCiz2V3zTPz1iASyme/3N16RF/XH5Oz+KJKZ9xSc/2vDRhDOFB/gAcyjrK+GenM+GVj/j2rScq7FY8evMIronrUWvfDqeLf7wzjx5tmxESUPV3PNjfl//cd2OFYy1veoJr4rrz7N3XVnlNy0bhTHv8diKCA8qP7T+Sw/VPT+b12cuY/NhtGNJtubZpEslHz9yDzeIJW2pInNWnsfWDZz7rced/LrFY5H0AvxzJYMpXi2jRKIqh3bvQrknjcyrTYZEGjcJCaRR2siKP1pob453l/19QUkJRqbsUpEuZOBxOXKZZ/m+73d3Wx8uGlBJfLy+kFPh4e2FISZCfH0F+vtVmNWutWb7ph5c/Sbw5udqJrnmvUMclfCPg8uqaaIiXruyP1aC77jk9PMGITxijNe/Wdj+EZFktTb4CHq++A6IM5Hxz6MOjWfVGrUoB0nA8B/jXOCdEWo2dJCf9KOImrtboITW2E7wo4xI6KSWerLL07U03GcaRqNu10G8AtVkoW0lO+rGWNh7qEdNU7M/I4cMla/lk+Xf8cjgbVZ+qJXXEajH469hRxHVrc0HHDfD1Ztrjt9OmSVSF4z5eDS+soCGilOLdhakUl5Xx8JgRF3s6tWJ3upi5bB2ldqfHkK2CBWu2YhiSv992VbkRC9A0MpQnxo3i3kkfsmnXfgZ1OfPv6ZhhfVm9ZRdvf7max8ddfl4VSWIbhXP1oO7MXf09ZXYHfj41bgR6uMic9bLCUWx/SAZ49ZNSli+l9h3JZN+RTKJDghnYuQM927XGIut/i8/tSfUjyO/8eV427t6z+e2/Xftkbe2kFvO00NUasgAIxkjhcwlxCbOAHQjChOYqramLWnuxaZfzamqghPhIav0YNegCaxgsTecO4ie+qJQxj9Q3D1Zo0CnRRlh2f6F5ELihljlpU+maVQ0AU5MoBXWRwrpNSn2Tjp/wrdDiOxBZaO0LtCWDkVro2Dr0gUAk1qWdh3NHaU161lHeXZjC7JUbLki52eoQQjB6UHfuviruvIcUVBobCAv0IzIkoNo2JXYHS777kQ0/7SM82J/hvTvSvXXTCmUrk7fsZvehDEYP6s7OA4fJLShmVP+u+HhZMZXiQEYu7y5MoaikjKiwIO6/dmglr5TTZbJoXRrJW3YT4OfN0B7tGNK9fZU+hX1Hspm7ahO/ZucxpEd7RvbtVKG07S+Hs1m4ZitDe7anayt3vHFJmZ13FqbQvlkjRvTtVN524ZqtZOUVMLJfZxas2cpP+w8zqEtrRg/uju9p5XJdpknylt0s/u5HCkvK6Ngihk+WfYfLVBUM2eJSO5+v+p4tew5is1q4ckBX4rq1K38vxaV2/jc/uZLn22IYjB85gKjQwPJjBzJy+PzbTRzIyKFPxxZcM7hHhWpvTpfJe1+nEBEcQLtm0cxdtYniMjvDe3Xkkp7tsVrcz9Se9Exen72MjKMFoOGlmYuIDgtizLA++HjZsDtdzF31PT5eNkb27Uzyll34+XgR1829WZaatocNP/3C+JEDiAwJLL/Pc77ZyFWDutGphVsQKL+ohC9WbSImIpiQAD9Wbd5JQXEZ1wzuTp+OLUnbe4il67dRXGYnrltbhvfq2GDKu/58OIvWjSMreDdPMKBza/75p6sJ8ju7csA92zWnXbMoXpy5iBsv6UXr0xaP9U2Qvw+FJfYGEebooWbO2pDdPifR0enOxCt88F4rpGhx6rmMvHy+SFnHih+20qddG/q0a0NwQI0OvgbFnvQj2b+U5Q6qS1vTtH4kLY6ngOa1NI0AHgBAn4GEgBCvsP6tmvcCk5N+JC7hY+C2WnqLRus3pHC9QVzCMTRHcJfujoLs4LpOSsM3rJ18oNaGqUmrGJzwGYIxdejWW2gxChjllluo21xOmdNKlZL05Zld5eFMsDucHMjMZfXmXSxal8bX69Jwui5ukRIpBZf26kjSo+MICTi7H8jzSe6xIu55cQZL1v9Ik8gQCorLeOrtuUyaMIaEG4ZjMSQuU/H2/FV8vTaNj5auJXnLbnq0bUbv9rE0iwpj5fc7GP+v6QghaB4Vyv6MXN5dkMznz0+gR1v3n50Su4OJr87k0xXrCQ/yp6jUzssfLyLxrmt59JYRFQx8h9PFFX99g1K7g7zCEt74bDl/uXoIbz9xR3mbjTv28eS0z3l4zGV0btkYKQQHMnJJfPcrurdpWsGQffmTxXy/cz+Nw4NxKUVhSRlvfb6CG4b24r2/31XBaHz0rdlM+/JbWjWOJDo0kIVrt/Jrdh692sWWtzlWVMot/5zGqs07aRkTQandwX8/X8Hfx1/JM3+6GoCsvAJe/nQxRceT15TWlNodCAQDOrcqN2RXb9nFLf+cRlGJncjQQKYvWM1/56xg1rP3lSfmFJXa+cf/5lFUaicmPBgpBUdyjvHarKU8fcdoEu+6BoB1P/7MzKXrKC1zIKXkhQ8X0iwqjCHd2xHbKJzCkjKem7GA0jIHYcH+/LDrAFcN7FZuyM5cuo63v1pF7/ax5Ybsqs07eXLa50gpyw3Zg5lHef6DhThNF0ppfLxspGfnMW3et9w9Oo73v15DSIAvR44e4+WPF/PShJt48MZL6+FpPTecLpP9R3KICg3C21bZtAgN9OPPV1feoNufkcO2XyooWxIbHYZ/FQmJd14xmGUbf+KxpNl89MyfCfQ9P+WnD+fks3TDNnq3j8XLenJ3paTMwbZffq0QWuDv60WzqDBkQ1lN/AE5p0CP7TMSM7reM2m4TZlrhRTRp58vKC5h5Q9bWbl5K22bNKZnm5Z0bN4Mm7Xhbrv9/OvhgjX7fu64IHFs3eQU1r1eKuIm/p9Gf3QeppOuvL0n1aWhEvpp6TYEw2tt7CYIwdlU19Ia8c+6NlZeTJAOBgB1ywg8O3K1Ke+tvdkfF601x4pLMU2Fj5cNm9WCpYpkFa01ZQ4nDqeL4jIHRaV29mfksGLjdr7fuZ+Dmbkczjl2UWJgq6J3+xZMShhDo7CLIx1cVGrng8VriD6erCKFIL57O7q1borWmtnfbGT5xu2Mvaw/T952Bdn5hUx87WOSvviGYb070KXlSXWFY0Ul7DyQwQM3DmdE387lXq3lG7eRV1jCZ/+6j76dWpK2N50/vzSDuas2lRuy32zawacr1nPTsD48MXYUJWV2HnrzEyZ9spgbhvaqkE3tcJnccmk/xgzrQ0buMf7y8gy+SP6Bh8dcRofYmLO6D06Xi4FdWvO38VdQVGon4dWZLNuwnZS0PVw5oCsAR3Ly+WT5d3Rv04zZz95HgK83aT+nM+KRVyv0tSc9k+Xfb2fcZf1JvPtaSu0OHv3vLD77ZiNPjBuFt81K06gwlrz6KObxpJ5vfthB4ntf0bF5I5pEugVVco8V8e8PF6K1ZuYzf6ZjixiSt+zmb1Pn8MqnS5j86HjkKdvTSikeuXkEI/p24mBGLtf+PYmZS9fxl6uHEBMezDVxPejSqglXP/lfHE4XS159FJvVQkx4xWfvUNZRLBaDl+6/iX4dW57V/QTIKyjh7SfuoG/HFizbsJ2npn3Oq7OW8saDtzJqQFe2/ZzOXf95j6TPVzLxhuG/WUPqtVnLeHdhSoVjE28Yzl+qMHqD/X1JvOsabvnnNL5YtYk7rxh8zuNn5hXwweI1BB1fcB0tLGbJ+m1k5xXw34fH4W2zlHtl96Rncvtz7yDlyXvdKiaC/z48jqZRoVX27+H8U3dD9qZEmzU7t6d24e+S5i6SpxwCSHvn8X1d//TClTZtWSpENUaUht2HfmX3oV+xWix0jG1Kt5YtaNMkBmsDCpren5FZtGLLL52XvTY250yuM1OSPpaDE0bX0fNYV+xKi9tY9kpxnVonTzmk4u6/USKXUxdd2rPndVKS1tS59crJuSru/qskMhnOynCuDYdC3cDayT+fh75/N2gNP+07zIszF+HrbSUkwA8fLxtBfj4YhsRUGofTRZnDSUbuMY4WFpOedZTDOfkcLajbI3ih6dyyMVMeu42urc/nGqlmyhxOXpy5qMKxx8deTueWjVFKs27bXoL8fbjv2qG0aRJFmyZR3DZyAE9O/Zztv/xawZAFeOTmEZXUDoSQKK1Y8+Ne2jaLZlT/Lnz14gOEBZ7c5UreststZ3TdMDq3dHv2JiXczOQvVpJzrKiCISuF4NGbRxDg602H5o0YPag7079aze5DmWdtyAb5+XL36Hh3hjcwfmR//po0mwNHTv4pzThaQEmZnQ7NG9HiuKxR7/ax+HrbCD7Fmx7s74tAsHnPQX7YdYBL+3TkvafuIiuvoFwpwGLI8pCHQ5lH+XjZd3hZLbz1yDiaRroNioOZR9l5IIMr+nfl8n5d8LJZaBkTwbQvv2V+6hbefGgstlMM2fDgAO6+Kg5vm5U2TaK4Jq4Hi79LY096JjHhwQT5+9CjbTO8rBaUUvRoezJB+VRCAvx448FbuWpQt7O6lydo3zyakf06Ex7kj9UweOOzZbhy8rlj1CAC/XyIjQ6neXQ4e9IzsTucFUJDfkuMH9mffh0r1tc58QxXRd8OLRh3WX/+89EiLu9foxpjncjOL+S9r1MxpMTucLLvSDY3DO3NpAlj6H2iqMpxQ7ZZVBjP/fm6CvG5gX7ehAX9dnacf4/UzYocmmiRGdl3m0JEIUHCIMvQB+c6Vv13G0Da+0//0OHO5wb6SusyKUVsTV05XS627t3H1r37sFgM2jRuRKfY5rRv1gR/n/OzTVAXfv71yNFtB/d3Xfbarb/W3roSWuVF3CZCs8MEDK+H6WiBuIfUpFVndFXK1NVi8MRxWugZUHUJ13NCMEfJiL+d8XUpU9PUoImXSqkX4Q6xqC8KBdxGytTV9djn7xIpBf06teS+a4dy/ysfcTDTrbMshCiP4tBQpUZiQ0NKwfBeHZn82PhKSVYXmiA/H15OGEPTCLcXUAhB++aNMKTE6XKyJz0Tb5u1wvZ6eJA/CNh3pOJ62WIYXNKjfaUx7rpyMFv2HOStz1cy9ctvadEoglsu7cuE64aVt9mfkYOvtxchgSe/9oO6tKZfx5aVZKJ8vW0VdGQbR4TgMs1zWrBYLQYh/u6xBRARHIDVYuFo4ck+O8Q2omNsYxZ/9yOvzVpKk8hQvl67lZIyB4O6tC5v1yQyhKduu5Ip875h7LNv4+ftxch+nUm4fhjW02Kg8wpLeOjNTziYmctrE2+mT8eW5c9zzrEi8otK+Hj5dyxal4Y47kUrLC7D7nRWiqTy9bJVyKYPCfDF6TLJP8NiHo3Cg+jcqnpDrK6EBPiVv19/Xy9sFov72HHnj8WQ+HjZ0FqTV1hy0Q1ZKQXeNisldgcupTg9PaqkzMHm3QeIbRRO44iTMuR9O7Tk6sHdz2is+6+7hDU/7uFf783npmF9zmneHWNjmPtCAuFBARSWlHLvpA8pKXPQqnHln6qQAF+uHNjVo1rQwKjTp2Ezc9u6hDjlF0MKl6kGA+Xi8ztm/GNPh/EvDPTzMhYJIer0VLpcJjsOpLPjQDoAoQH+tG4SQ5vGMbRpGoO39cJ8MX/YvXdXWubungsS7z378kPbEx16wCOjhWF/CyHuPofp5AjUnWbK1K/P5mIzNWkOAyfskYaYB8SewzwqdIsWk1R05v8xZ/LZBUWuSfpeDX6otyFcn2oYWA9z2qsU17Jm8vZ66OsPgSElVwzoytS/3s4TUz5j+75f0bpGteAGh8UwuKxPRyYljLnoRiy4Dbj4bm1p37xRpXMC8LHZMJWqEEtsHl8suE6LL/bxslYSewdoGRPB+0/dxarNO/lu+y8s37id596fT0FxGS9PuAkAb6sVh9NVvtUOYHe4yCsqITTQr8YqZyd2pE99DmxWC4YUFRY2dqer+oWO4LS4dvc/Tm1uMSSDurRm+75fefb9+XhZLQgBf7pyMPeMjj85tsXCwzeP4LK+nVj7416+3byThWu3krp1N2kfPlceC13mcPLuwmQWrUvjgRsvZcywvhWmYLUYWAxJ/04tuXl433IvmstUeNusVYbWVEVV77imBZ9FykoGN1A+/qnayjUqfFS4p6L6vAHdMBaghpS0ahxB8pbdFJfa8fOuaMpu2/cr97/yES9NuKmCIXs2xIQH8/Tto7n5manloSRnixQCHy8bvt7u11+uHsKtiW+zJm3vOXvVPVwY6vRN1rrKrepKx3bMfPqINkL6upT5GoIzFno9WljEhh27+XjFKp6d8Smvz/mSz1alsmHHLjLz8uv9y+o0TZZ/v2XWC/df0v6cjNgTrHu9VKVOuUdo7gYqS0nVjNJafKEM2eNsjdhy1k7ZolRpV7R4ESg6l660YIFSdFOpSX+vtrpYXUl986CZEhGH0Pdx5vfnBEfRPKlKnN08RuzZccWALsx9IYFhvTr8pkTdA3y9efDGS/n8+YTyxJiGjMVi0Kt9c44VlXIo6yjgjk/dvPsAUgo61bB9eiovfbyY12Yv4/ohvXjrkXGsmfYUQgqWbjhZxK5982jKHE52HDgCuI2lSZ8sptn1j7F6864znntIgB/eNis7D2aglEZrzZ70zHMqMf7D7oNMn7+aO0YNYsmrj7D41Uf44f1E/ve3O8vDAQA27tzHw29+gr+PF0+MG8XiVx7h9pEDSM/OI/34fQSYtWLD8Qz23jzzp6vxPk3yLCY8mMjgQJTSjL2sP/deM5Rr4npwOCePwzm1qhBWi9ViUFBcdsZx4s2jwwBY/9Mv5cfSfk4/63k0RK4c0I3t+w4zP7Wi9LnLNPl0+XcUlpbRvU39hALFdWvDdUN68tLHi+u18MoVA7rSrXVTXp+9DLuj4elBe6hMnTyyzhD7HplvLUaIcr0XKcWWqh6dTdPvdQKPdbv9+Q1Wi2WykCLsbCamtSYzL5/MvHx+2L0Xp8vkl8O5xEZH0SwygubRkTSPjKRpZHiVXozaOJCVXZK2b/9tM566/ouzmV9NmKmT32PonZ9I0/cegRivoTdQnS5QBlp/rSzqDVZNq7/yqmveK1Twdwbf/7IUxniNvl7AAKi041Np+rg97V8ooeeQPGVHvc0JgESlknmbUQ/MMIr0TUqosUKLIdQcCmEK2KSF/kxJ27t10cL1UDPtmkXzxb8n8tbnK3h11tIz3j69kAghaN8smn/86Wqui+/ZIEu/VoUhJdfG9eSzlRt56M1P2L5vCIcyj/LB4jUM6dauzlqaJXYHU774hqMFRYzs25mNO/djmppup8QG33b5QCZ/8Q2PJc1m275fyS8sYebSdXRq0Zie7WoTVKlM7/axtGvWiEVr0/jb1DmEB/kza8UGSs/hh71ZZChRoYGkpu2hsNStNhDo403vDrGMHtS9PJM/0M+HRevS2HUwg3tGx+N0mazctIPQQL/yxKqsvAKeff8rCotLcThNnp4+t3ycR8aMoEVMBK1iIrh+aC9e/ngRdzz/Dpf16cT81C1s2LGPZ+4cfdbJUZ1axLDrYAbX/T2Jji1i+L87RtepCMfIfp154cOFTJ23CqdLUeZwMnvlhrOaQ0NlQOdWJNwwjH+++yX7jsorDz8AAA5dSURBVOQwsm9nHC4Xs1ZsYNmGbTxz19VEh1ZMldiw45cKyVMniAkPrqBmcTo2q4Wnb7+KNWl76l3+76+3Xs6EVz9i7uofuPXSvuXH8wpK+HptWiVvvs1iYWCX1p7SzxeJulmAC6aXqOEJ7xkuHY+JvzT0Tufqyd/DW9VesvXD/5vd/vZ/r/A2mGSR8g4hxDm7fopKy9i27wDb9p1UfhJCEBYQQGRoMNEhwUSHhhAdGkxoUCBhgQEE+flV2OaxO5069ceflm7eUHL9ujlj6qZMcDasmlGmIAlIYnhCmFEm+iKI1uhIhC4WkGEKuYvkpG2cgRrXGZM6NU+5P6i39KgHvCjQXQxDt9RaRB3XagWp84UWx0yt9mB6/8S618/ffTnB4rfsJswEZupOiTbCs7oZJq21IBotbAihBSrL1HI/3o7NrJh+7LzP6Q9GkJ8Pj48dRa92sTzzzjw27z54wUvK1oa3zcoNQ3vx9O2jadc8usFkZlutFgL9fPD39a5RmH1g51a8/uCt/P3tz/nH/+YhhFse6uWEMRW0Tr0sFvx9vKvc7n7wxkvJLyzmg8Vr+XT5eoQQjOjTqYLkUuOIED54+h4efutT/vPh11gMSZdWTXh14i2EHTeyhHDfz9M1PkP8/fD1tuFzygLB38eLf951DRNfm8nkuSsJ8vdl9KBuHMnNr/RjHeDrja+XDa9T4ga9bVYCfL0J8jvZNregCD9vLw7n5BMRHIDFYrD3UBbvLEwhectupv71dny9bbRpEslrD97CP/43j4mvzwQEjcODmZQwpjyppqC4lJIyB4ZhsHDt1vIxLIbk5uF9aRETgWFIHrtlJKV2B+9/ncrS9dsIDfLnvmuHcs/o+HIdX5vVcMcN+1V8X/4+Xvh62ypJST0xdhQ5x4pI3rqbAxk5TLxhOKGBfhhSEhroh81iVKln3L55I/5x52ien7GAf3+4kOiwIK4c2JVZKzbg630yjE4KgbeXlRD/kwV7fLys+Pt4o1XFWkOBvt4E+fuclTPnfGCzWnhi7ChiwoKZ/MU3TJ67EiklYYF+TEoYw+jB3SvoJwf5+/LG7OXA8kp9XTmwG589dz+GlPj7eFfSJAZ34YJHbx3JU9PmVin5Be7dhepkuny9bIQE+lX63l3auwN92scy55uNXBffAy+bFT9vLw5m5jL+2emV+gny92H+Sw/Su32LSuc8nH8uyK9C5z89P9AmjRcNIePOtg+nyyTt57PJw4IAHx+CA/xcLWMarWgeEfPMjGeu3ni28/Dg4WKgtV4H9D9f/ZfaHXy4ZC3//vDr8kSwi4m3zUr/Ti15fNwVjOzb6bxW8TkXSu0OvGzWOhnYBzJysVqMSnJNFfqyWqv0ToE7JvRARi5eNgvNo8IqGASnkp6Vh5fNQligf6W+lNLYnZUz3O1OF1KIcvH/U9l9MING4cEE+HqXL3RO/TxMU+E0zUqe8jKHE6thYBgSrTVPvT2X/36+gtn/up+rBrpjD12mYtyzb/PtDzuZ+0JCueYqHNclzcjBYkiiQoIqGHvgDp+oKtShqs+jqKSM9Ow8osOCCPavvPljKoXDZVYw5k/cF0OKKg1Tl6lwnfa+naaJUrrGmOSC4lJyC4ppEhGC1WLgcLnfw6kJRFV9HkrrSuoEJ+7BuSR65RWWMOj+f/PQTZdy7zVDz7qfqjick4/NaqlQ5cuDh/rmgizjtr3/f2uB+Hbj/jXIZpHPeFktlwrO3UNbR8oKS0rfPVZUOunA3GdrF/H34OEPiI+XjT+PjufS3h35aOlavlj9A7sPZmA/h5jIM0UAvt5e9OnQgruvimNE305EBAdUa7A1BM7EgDgRI3m2fXnbrLRrVkmuuxI1Jb9IKaocpybDq+0pY1a1oDAMWWWsdQXDVgiEECilySsoJuPoMaQQlNqduEyFYchKUoxWi1FjQp8hZZ3vv7+vd5UJeRX6slV+DzXdF4shK3nyrIZRfRDZcQL9fCqoWFSVAV/VuCeSkirNuwHLblW3aPPgoT6p/1+IoUMthu44SivRXmhyTYtcxqq3KkS0d71nUgtpusYagtuklO3q0u2ZemQFYpMWfKSk42NWTT8jXVgPHhoa59sjezoZuceYu3oTHy1ZWyE55XwR7O/L5f07c21cT0YN6EqgJ9bsd8dP+w9z2cOvUuZw0LZpNBZDUlBcyk/7D3P9kF6899RdlTLdPZx/zqdH1oOHC0H9GrKdbrLJ0Ig3NeJUMUS7BfMZZ8q0KqPaO93+fF+rNEZJqYdIYfQVgiqj5utgyGag+UYIvjVNltephKoHD78RLrQh6x4TXMrkwJFcPl2xntWbd7LrUCaHs/POuf64zWqhSUQIPds1Z2Tfzowe1J3wYP8GG0LgoX44lHWUBWu2su9wNk7TJNDXh8Fd2xDXrU2D9iz+nvEYsh5+69SrIWvETxintLinilM5OiVpDIgaf/2GJiZacn72aWMVZictRSeEbi6F8NcCP9Olgn785XChEBSiRT7oQhB7pGC3y+naxbppZxdA68HDb4CLYcieTl5hMenZeew7nMPaH/ewassuso4WYHe6cDhdOFymW5f2uK6llAJDSqwWA6vFICzQny6tmtC5ZWO6tW5Ky5gImkaFerxwHjxcRPKLShj24MskXD+Mu6+Kr/0CDx4aGPVqyMq4hJc1VFlmQwt9+4mytr8njPgJ47QiSGFdiHTFkjw5mX4PRWF1BRlCd9XIcIE+YqrSb6TwmSAQeaZUiw3NcI30lug9rpTJK434hDu01qHKsL2PXUhp2G8RQhaYKUkzjbiEe8yjER+yPdEh4ybep1KSpsm4ifehlVSSLShtxyJ2QWQZKquvoWRjDW0FZJtCzMM0GxuGbmxq1uIlJQ4ZhdL5UqhrhZCZZkqSW7smfmILpHAaSg8yc8PnEZIdi9NyjPVvZhI/YSB2Yxuh2ClW/dBKSIwOSoj1SPshTK9IUt76iSEPxCGcedKUQwGUUfIOq2aUET+hqVTiJiEoME1zvsVqdNOmaAE4zdSk9y3xE4cpTVuFudYiRZhrdeRq4jLjwNyNsHnj0sqwMFQr7aeEa54Fa0etRSvQpabFukCarr+gdZkSzjmkTD9y8Z6I80NDMGSrwmUqDufkkZVXQHZ+ES5ToY4nznjb3Jn4wf4+RIQEEhroV2XmsQcPHi4eDqeLpRu20aF5DK2bRNZ+gQcPDYx63sdT1YvvS1th/Y7VEEiUWokmKnXKVKQKk1p3BcDLGWoxdGMtRJRKSZqmoQVWbz8hxF4zNWk6yVMOaSH8VErSNKXpxpAJfUzYp0pcb1tcjv7SYh+r8iLfMTEPEf9ADy1EEyMs53JG/NVPoG8FBEIbKnXKFKlFX0MarXEoX3xyDUOLjmZq0hxQeWZq0nSUthoWWmotW0kh78CugizabCaFvkWlTpkqtDjG0AnHU0pVI0wVqbVuaYRm326Rogk+pjtzxOm1WVrVnbLYvBNpTZNa9lIpSdOkVkPAEmzRZjMAaZpdDJcRq0yxQKGTLdrPrVShRYSS+jvTsH5uGPIKpWlrpiZNRyoHA++LVFp1UylJ0yRymEsLk/jcbhKjqyFslxhaXYqvM1+jw5UlcjraK1KhO5ipSdPN1MkfoRwBQrBTNcqaLIX1+gv/HPxxsRiSZlFh9G7fglH9uzB6UDeuievBTZf0ZvSg7lzSsz092janSUSIx4j14KEBYrNaGD2ou8eI9fCbpV4NWYmxsqrjQuhNv08R+0SlDLlWxk94FNNVYX/UZUqN1qHG4Am3orUDQGt9mRE3cTwAmpYyfmKikHq/RclQFBlsml7iSp2yBC0k2xMdCOthi1DhaDI0qpFRVnaVFnrxieuN+IQ7BOJQhUKQoprwDYFTIdcaGENOTJH4CU2UNHsDp2uj5KPJVUqdjHVe93qpEBwQyPzjn6XrRL84jEpjSoOxUou7XF4+a8uPaa6RpvNxU7AbrW0yLuF+rUVHGufmIoSjvD8ZsdZQuo/QuhitfTXCworpx6TgB6lyHgaXBYTFGDzxLwxO6Oe+HYyURyJeVej5tXxoHjx48ODBg4ffCfVqyLpSktZIof4HolyzR6C3qWLXi/U5ToNh6MPBUpndlFPNkobsJTQHGJIwwFAyDtO1ByGOmqlTPlWpU6a4L9BppjbXMvRObxD7lVSvaC0jXabeKaW+hMEJ/Yy4ieOF1LkMfqCb1Gq4S+udAEJxWENTtHQvCAS/mMmTPzBTkuaaWu00LJZLjEJ1mSn0T9XON+WtDVrS/Pj1TiReKOGHsgSc3tRMnTwPIfpWOGbqA6ZS7lhkoYMY+kATtPDDbj+iEN2In9BBHC+Jq4SYhZT7Tr1ewEal5WeGkp1BulTK5KlAEXPmmGjCiJ/QFIQPqxJdWuh2pjz+3rUoc3ttaauU8ZkURh8Qpom5AixH3G1YLgSpSFl7iR0PHjx48ODBw++Cek8RNpOnfqINy00WzL9pre5WKZMfZNPvVP5q1Rv5ymCJxWZ0VH7yHTN1ygKQFlOIVNZN+1U5zS/L22ZHHpUYuy1YWoLNXxliHqumFCnDtYS1kw8oSLYY2tdMSZppJk/52GKYoUqZ35I85ZBSzDdLXUuU0/WpcrkWALpC3ylT00zEbtPQmayevA5AmeorAPLDc0xtpCiXOR9AeXtPcpleG1R01nTDKdsozSychlsezebajirdU97WcP6VQsfB8nFs7MXb9SOAsokphkt1VyWOJDZNL1EWc7ZFiCZm6pSPTIvzO2TREWWWvEeh6fZUq9I9Qhv5CLzMRpnvKkPMA1BKfECnRJsynFMMrbsqp3UygNJqMjJyg4n8ysT8krXTspSpV1mEq406Gv6eMvV8C5aWCEcL/IwsE7HGTIn8ApfRcGutevDgwYMHDx7qlYarNO7Bg4dyGmqylwcPHjx48HAx+X8Xq1zYoK5w/QAAAABJRU5ErkJggg=="
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "\n",
+ "<h5 style=\"text-align: right\">Author: <a href=\"mailto:j.goebbert@fz-juelich.de?subject=Jupyter-JSC%20documentation\">Jens Henrik Göbbert</a></h5> \n",
+ "<h5><a href=\"../index.ipynb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">List of Extensions on Jupyter-JSC</h1> "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you want to use any of these extensions, feel free to use our [examples](https://github.com/FZJ-JSC/jupyter-jsc-notebooks) as a starting point. \n",
+ "\n",
+ "You can list the currently installed extensions by running the command in the JupyterLab terminal: `jupyter labextension list`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "JupyterLab v2.1.4\n",
+ "Known labextensions:\n",
+ " app dir: /gpfs/software/juwels/stages/Devel-2019a/software/Jupyter/2019a.2-gcccoremkl-8.3.0-2019.3.199-Python-3.6.8/share/jupyter/lab\n",
+ " @bokeh/jupyter_bokeh v2.0.2 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " @jupyter-voila/jupyterlab-preview v1.1.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " @jupyter-widgets/jupyterlab-manager v2.0.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " @jupyter-widgets/jupyterlab-sidecar v0.5.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " @jupyterlab/git v0.20.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " @jupyterlab/server-proxy v2.1.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " @jupyterlab/toc v4.0.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " @krassowski/jupyterlab-lsp v1.0.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " @krassowski/jupyterlab_go_to_definition v1.0.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " @parente/jupyterlab-quickopen v0.5.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " @pyviz/jupyterlab_pyviz v1.0.4 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " @ryantam626/jupyterlab_code_formatter v1.3.1 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " bqplot v0.5.12 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " dask-labextension v2.0.2 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " ipyvolume v0.6.0-alpha.5 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " itkwidgets v0.27.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " jupyter-leaflet v0.13.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " jupyter-matplotlib v0.7.2 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " jupyter-threejs v2.2.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " jupyter-vue v1.3.2 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " jupyter-vuetify v1.4.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " jupyterlab-control v1.1.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " jupyterlab-dash v0.2.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " jupyterlab-datawidgets v6.3.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " jupyterlab-gitlab v2.0.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " jupyterlab-lmod v0.7.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " jupyterlab-plotly v4.8.1 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " jupyterlab-system-monitor v0.6.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " jupyterlab-theme-toggle v0.5.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " jupyterlab-topbar-extension v0.5.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " jupyterlab_iframe v0.2.2 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " nbdime-jupyterlab v2.0.0 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " plotlywidget v4.8.1 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n",
+ " pvlink v0.3.1 \u001b[32m enabled \u001b[0m \u001b[32mOK\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "!jupyter labextension list"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Dask (only on HPC Systems) <a class=\"anchor\" id=\"dask\"></a>\n",
+ "https://github.com/dask/dask-labextension"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ "<img src=https://raw.githubusercontent.com/dask/dask-labextension/master/dask.png width=\"640\" style=\"float:right\"/>\n",
+ "</div>\n",
+ "\n",
+ "An [extension](https://github.com/dask/dask-labextension) to manage Dask clusters, as well as embed Dask's dashboard plots directly into JupyterLab panes. \n",
+ "Watch [this](https://www.youtube.com/watch?feature=player_embedded&v=EX_voquHdk0) video until the end to unterstand how to use Dask in JupyterLab. At the moment we only offer to use the panels inside of JupyterLab. \n",
+ "We have introduction notebooks for this extensions [here](https://gitlab.version.fz-juelich.de/jupyter4jsc/j4j_notebooks/tree/master/001-Extensions) (or open the gitlab extension on the left sidebar)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Git\n",
+ "https://github.com/jupyterlab/jupyterlab-git"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ "<img src=https://raw.githubusercontent.com/jupyterlab/jupyterlab-git/master/docs/figs/preview.gif width=\"640\" style=\"float:right\"/>\n",
+ "</div>\n",
+ "\n",
+ "A JupyterLab [Git](https://github.com/jupyterlab/jupyterlab-git) extension for version control using git."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Table of Contents\n",
+ "https://github.com/jupyterlab/jupyterlab-toc"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ "<img src=https://raw.githubusercontent.com/jupyterlab/jupyterlab-toc/master/toc.gif width=\"640\" style=\"float:right\"/>\n",
+ "</div>\n",
+ "\n",
+ "A [Table of Contents extension](https://github.com/jupyterlab/jupyterlab-toc) for JupyterLab. This auto-generates a table of contents in the left area when you have a notebook or markdown document open. \n",
+ "The entries are clickable, and scroll the document to the heading in question."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Jupyter ThreeJS\n",
+ "https://github.com/jupyter-widgets/pythreejs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ "<img src=https://raw.githubusercontent.com/jupyter-widgets/pythreejs/master/screencast.gif width=\"640\" style=\"float:right\"/>\n",
+ "</div>\n",
+ "\n",
+ "A Python / [ThreeJS](https://github.com/jupyter-widgets/pythreejs) bridge utilizing the Jupyter widget infrastructure."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "## Leaflet\n",
+ "https://github.com/jupyter-widgets/ipyleaflet"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ "<img src=https://raw.githubusercontent.com/jupyter-widgets/ipyleaflet/master/basemap.gif width=\"640\" style=\"float:right\"/>\n",
+ "</div>\n",
+ "\n",
+ "The [Jupyterlab Leaflet extension](https://github.com/jupyter-widgets/ipyleaflet) enables interactive maps. \n",
+ "You can find several example notebooks [here](https://github.com/jupyter-widgets/ipyleaflet/tree/master/examples)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Sidecar\n",
+ "https://github.com/jupyter-widgets/jupyterlab-sidecar"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ "<img src=https://raw.githubusercontent.com/jupyter-widgets/jupyterlab-sidecar/master/sidecar.gif width=\"640\" style=\"float:right\"/>\n",
+ "</div>\n",
+ "\n",
+ "A [sidecar](https://github.com/jupyter-widgets/jupyterlab-sidecar) output widget for JupyterLab"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Voilà Preview\n",
+ "https://github.com/voila-dashboards/voila"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ "<img src=https://github.com/voila-dashboards/voila/raw/main/voila-basics.gif width=\"640\" style=\"float:right\"/>\n",
+ "</div>\n",
+ "\n",
+ "[Voilà](https://github.com/voila-dashboards/voila) turns Jupyter notebooks into standalone web applications.\n",
+ "\n",
+ "Unlike the usual HTML-converted notebooks, each user connecting to the Voilà tornado application gets a dedicated Jupyter kernel which can execute the callbacks to changes in Jupyter interactive widgets. \n",
+ "\n",
+ "This extension allows you to render a Notebook with Voilà, so you can see how your Notebook will look with it.\n",
+ "\n",
+ "You can download a test notebook with the following command: \n",
+ "```\n",
+ " $ wget --no-check-certificate https://jupyter-jsc.fz-juelich.de/static/files/voila_basics.ipynb\n",
+ "``` \n",
+ "and get a preview of it with the button at the top of your notebook."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Quick Open\n",
+ "https://github.com/parente/jupyterlab-quickopen"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ "<img src=https://raw.githubusercontent.com/parente/jupyterlab-quickopen/master/doc/quickopen.gif width=\"640\" style=\"float:right\"/>\n",
+ "</div>\n",
+ "\n",
+ "[Quick Open](https://github.com/parente/jupyterlab-quickopen) allows you to quickly open a file in JupyterLab by typing part of its name. Just click on the lens symbol at the left sidebar. \n",
+ "<span style=\"color:darkorange\">Takes a long time on HPC systems.</span>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## JupyterLab LaTeX Extension\n",
+ "https://github.com/jupyterlab/jupyterlab-latex"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ "<img src=https://raw.githubusercontent.com/jupyterlab/jupyterlab-latex/master/images/show_preview.png width=\"640\" style=\"float:right\"/>\n",
+ "</div>\n",
+ "\n",
+ "The [LaTeX Extension](https://github.com/jupyterlab/jupyterlab-latex) is an extension for JupyterLab which allows for live-editing of LaTeX documents. \n",
+ "[Here](https://annefou.github.io/jupyter_publish/03-latex/index.html) you can find a short example."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Code Formatter\n",
+ "https://github.com/ryantam626/jupyterlab_code_formatter"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ "<img src=https://jupyterlab-code-formatter.readthedocs.io/en/latest/_images/demo.gif width=\"640\" style=\"float:right\"/>\n",
+ "</div>\n",
+ "\n",
+ "This is a small Jupyterlab plugin to support using various code formatter on the server side and format code cells/files in Jupyterlab. \n",
+ "Please read the [documentation](https://jupyterlab-code-formatter.readthedocs.io/en/latest/index.html)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## IPyVolume\n",
+ "https://github.com/maartenbreddels/ipyvolume"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ "<img src=https://cloud.githubusercontent.com/assets/1765949/23901444/8d4f26f8-08bd-11e7-81e6-cedad0a8471c.gif width=\"640\" style=\"float:right\"/>\n",
+ "</div>\n",
+ "\n",
+ "3d plotting for Python in the Jupyter notebook based on IPython widgets using WebGL.\n",
+ "Please read the [documentation](https://ipyvolume.readthedocs.io/en/latest/). "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Jupyter Lmod\n",
+ "https://github.com/cmd-ntrf/jupyter-lmod"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ "<img src=https://camo.githubusercontent.com/2a1fa198b6b7f35c7b9751664dfe5102fa5aa595/68747470733a2f2f692e696d6775722e636f6d2f3148444837694e2e676966 width=\"640\" style=\"float:right\"/>\n",
+ "</div>\n",
+ "\n",
+ "Jupyter interactive notebook server extension that allows user to interact with environment modules before launching kernels. \n",
+ "The extension use Lmod's Python interface to accomplish module related task like loading, unloading, saving collection, etc."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Matplotlib\n",
+ "https://github.com/matplotlib/ipympl"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ "<img src=https://raw.githubusercontent.com/matplotlib/ipympl/master/matplotlib.gif width=\"640\" style=\"float:right\"/>\n",
+ "</div>\n",
+ "\n",
+ "Leveraging the Jupyter interactive widgets framework, ipympl enables the interactive features of matplotlib in the Jupyter notebook and in JupyterLab. \n",
+ "Besides, the figure canvas element is a proper Jupyter interactive widget which can be positioned in interactive widget layouts. \n",
+ "Please read the [documentation](https://matplotlib.org/contents.html)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## NDime\n",
+ "https://github.com/jupyter/nbdime"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ "<img src=https://raw.githubusercontent.com/jupyter/nbdime/master/docs/source/images/nbmerge-web.png width=\"640\" style=\"float:right\"/>\n",
+ "</div>\n",
+ "\n",
+ "Tools for diffing and merging of Jupyter notebooks. \n",
+ "Please read the [documentation](http://nbdime.readthedocs.io)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plotly\n",
+ "https://github.com/plotly/plotly.py"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ "<img src=https://raw.githubusercontent.com/cldougl/plot_images/add_r_img/plotly_2017.png width=\"640\" style=\"float:right\"/>\n",
+ "</div>\n",
+ "\n",
+ "Plotly's Python graphing library makes interactive, publication-quality graphs. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts. \n",
+ "Please read the [documentation](https://plotly.com/python)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Other Extensions useful for Jupyter Users\n",
+ "- jupyter_bokeh\n",
+ "- jupyterlab-lsp\n",
+ "- jupyterlab_go_to_definition\n",
+ "- jupyterlab_pyviz\n",
+ "- bqplot\n",
+ "- itkwidgets\n",
+ "- jupyterlab-dash\n",
+ "- jupyterlab-gitlab\n",
+ "- jupyterlab-control\n",
+ "- jupyterlab_iframe\n",
+ "- jupyterlab-theme-toggle\n",
+ "\n",
+ "## Internal Extensions\n",
+ "- jupyterlab-datawidgets\n",
+ "- jupyterlab-server-proxy\n",
+ "- jupyterlab-system-monitor\n",
+ "- jupyterlab-topbar-extension\n",
+ "- pvlink"
+ ]
+ }
+ ],
+ "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": "3.8.5"
+ },
+ "toc-autonumbering": false,
+ "toc-showcode": true,
+ "toc-showmarkdowntxt": false
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/02-Configuration/03-Environment.ipynb b/02-Configuration/03-Environment.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..da09c873b735ea88064b28be47010f2a4163ab94
--- /dev/null
+++ b/02-Configuration/03-Environment.ipynb
@@ -0,0 +1,67 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "f66d472c-49c1-47d2-8d28-99749aa5c770.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "ca8705ef-351e-411b-b988-6658cc06a450",
+ "metadata": {},
+ "source": [
+ "\n",
+ "<h5 style=\"text-align: right\">Author: <a href=\"mailto:j.goebbert@fz-juelich.de?subject=Jupyter-JSC%20documentation\">Jens Henrik Göbbert</a></h5> \n",
+ "<h5><a href=\"../index.ipynb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">Environment</h1> "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "da15f1a1-b8e9-449e-a17c-13006f5492a6",
+ "metadata": {},
+ "source": [
+ "# Modules"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ba0376e8-5268-4c5c-98ed-62065def57df",
+ "metadata": {},
+ "source": [
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "08a89c95-d669-4b51-ae2c-b085612fb73a",
+ "metadata": {},
+ "source": [
+ " - [List of installed python packages](https://docs.jupyter-jsc.fz-juelich.de/github/kreuzert/jupyter-jsc-notebooks/blob/documentation/02-Configuration/details/List_PythonPackages.ipynb)\n",
+ " - [Howto load additional software modules](https://docs.jupyter-jsc.fz-juelich.de/github/kreuzert/jupyter-jsc-notebooks/blob/documentation/03-HowTos/Howto-load-additional-software-modules.ipynb)\n",
+ " "
+ ]
+ }
+ ],
+ "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": "3.8.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/02-Configuration/2-Factor-Authentication.ipynb b/02-Configuration/2-Factor-Authentication.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3e0a633dd2b8bda0e49a83d3940465d7b04c6ee0
--- /dev/null
+++ b/02-Configuration/2-Factor-Authentication.ipynb
@@ -0,0 +1,236 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "2087d183-9538-4c34-a9f7-5f6a5428c6ee.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "\n",
+ "<h5 style=\"text-align: right\">Author: <a href=\"mailto:j.goebbert@fz-juelich.de?subject=Jupyter-JSC%20documentation\">Jens Henrik Göbbert</a></h5> \n",
+ "<h5><a href=\"../index.ipynb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">2-Factor Authentication (2FA)</h1> "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ " <img src=https://jupyter-jsc.fz-juelich.de/hub/static/default/images/2fa/jupyter-jsc_2fa_img01.png title=\"2-factor-authentication\" width=\"320\" style=\"float:left\"/>\n",
+ " <!-- <img src=images/jupyter-jsc_2fa_img01.png title=\"2-factor-authentication\" width=\"320\" style=\"float:left\"/> -->\n",
+ "</div>\n",
+ "\n",
+ "## Introduction\n",
+ "2-Factor Authentication (2FA), sometimes referred to as two-factor verification, is a security method in which you provide **two different authentication factors** to identify yourself at login.\n",
+ "This process is **performed to better protect** both your credentials and the resources that you can access.\n",
+ "\n",
+ "In the **first login step**, you start with the usual entry of a good password. The service then confirms the correctness of the password entered.\n",
+ "This does not, however, lead directly to the desired entrance - but to a further barrier.\n",
+ "\n",
+ "The **second login step** prevents unauthorized third parties from gaining access to your account just because they might have stolen your password.\n",
+ "A quite common 2nd-factor is a **One-Time Password (OTP)** generated by a so-called **OTP-App** you install and initialize once on one of your personal devices.\n",
+ "This *OTP-app* then provides (in our case every 30 seconds) a new *one-time password* that needs to be entered on the login page.\n",
+ " \n",
+ "<div style=\"clear:both\"></div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ " <video controls src=\"https://multimedia.gsb.bund.de/BSI/Video/2-Faktor-Authentisierung_SD.conv.mp4\" width=480 style=\"float:right\"/>\n",
+ "</div>\n",
+ "\n",
+ "## Basic Principle\n",
+ "These two factors for authentication combine the building blocks **knowledge** and **possession** in the login procedure. \n",
+ "- **knowledge** - the secret knowledge is the password you enter. \n",
+ "- **possession** - With the *one-time password* you show that you are in possession of a certain device (e.g. your smartphone), because only the *OTP-App*, installed on that device, can generate it. \n",
+ "\n",
+ "<div style=\"clear:both\"></div>\n",
+ "<div>\n",
+ " <p style=\"float:right\">Source: Bundesamt für Sicherheit in der Informationstechnik</p>\n",
+ "</div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ " <img src=https://jupyter-jsc.fz-juelich.de/hub/static/default/images/2fa/jupyter-jsc_2fa_img02.png title=\"2-factor-authentication\" width=\"320\" style=\"float:left\"/>\n",
+ " <!-- <img src=images/jupyter-jsc_2fa_img02.png title=\"2-factor-authentication\" width=\"320\" style=\"float:left\"/> -->\n",
+ "</div>\n",
+ "\n",
+ "## Algorithm\n",
+ "The **OTP-App** can calculate personal one-time passwords completely autonomously from the outside world using a standardized and open algorithm for the generation of **Time-based One-Time Passwords (TOTP)**. \n",
+ "\n",
+ "The *TOTP algorithm* was published in 2011 by the [Internet Engineering Task Force (IETF)](https://www.ietf.com) as [RFC 6238](https://tools.ietf.org/html/rfc6238). The *TOTP algorithm* is a hash function in which a secret code is hashed together with the current time.\n",
+ "Behind the hash function is the HMAC-based One-time Password Algorithm according to [RFC 4226](https://tools.ietf.org/html/rfc4226) - in simple terms nothing more than a standard that forms a hash in a certain way.\n",
+ "\n",
+ "The calculation includes both a **\"secret initialization code\"**, that is known to both the server and the client, and the **current time**.\n",
+ "The final *one-time password* is generated from these two inputs and is valid for a certain period of time. (in our case for **30 seconds**).\n",
+ "The procedure can be implemented in such a way that slight differences in time between client and server are accepted.\n",
+ "\n",
+ "Hence, any *one-time password* is time-based, calculated locally, and always unique.\n",
+ "\n",
+ "<div style=\"clear:both\"></div>\n",
+ "\n",
+ "------------------"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to get started with 2FA\n",
+ "<div>\n",
+ " <img src=https://jupyter-jsc.fz-juelich.de/hub/static/default/images/2fa/jupyter-jsc_2fa_img03.png title=\"2-factor-authentication\" width=\"320\" style=\"float:right\"/>\n",
+ " <!-- <img src=images/jupyter-jsc_2fa_img03.png title=\"2-factor-authentication\" width=\"320\" style=\"float:right\"/> -->\n",
+ "</div>\n",
+ "\n",
+ "## Preparation\n",
+ "\n",
+ "To get ready to use 2-Factor Authentication (2FA) for Jupyter-JSC you have to **prepare** it ONCE: \n",
+ "- (1) **request 2FA** for Jupyter-JSC, \n",
+ " - (a) login to [Jupyter-JSC](https://jupyter-jsc.fz-juelich.de) \n",
+ " - (b) visit https://jupyter-jsc.fz-juelich.de/2fa and request 2FA \n",
+ " - (c) wait for a *confirmation emails* and click the provided *activation link* \n",
+ "- (2) **activate 2FA** for Juypter-JSC,\n",
+ " - (a) install an **OTP-App**, which supports the TOTP algorithm \n",
+ " - (b) communicate the **secret initialization code** to this *OTP-App* \n",
+ " - (c) test a first **one-time password** generated. \n",
+ "\n",
+ "... and then 2FA is ready to be used next time you log in.\n",
+ "\n",
+ "### 1. Request 2FA\n",
+ "Please login to Jupyter-JSC as usual through https://jupyter-jsc.fz-juelich.de \n",
+ "and visit the webpage **https://jupyter-jsc.fz-juelich.de/2fa** for requesting 2FA.\n",
+ "\n",
+ "Please read the notes on this webpage carefully and click the button **Request 2FA** to start. \n",
+ "A **confirmation email** including an **activation link** will be send to you directly.\n",
+ "\n",
+ "### 2. Activate 2FA\n",
+ "Please follow this *activation link* to instruct Jupyter-JSC for preparation of your 2FA. \n",
+ "You will be asked to re-login to your account to recieve a **secret initialization code** as QR-Code (and string) \n",
+ "for a required *OTP-App*. \n",
+ "\n",
+ "So first, you need to install an **OTP-App** on one of your personal devices (if you haven´t done so already), \n",
+ "which you plan to use in the future to generate the required **one-time passwords** for each time you log in:\n",
+ "\n",
+ "<div style=\"clear:both\"></div>\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ " <img src=https://jupyter-jsc.fz-juelich.de/hub/static/default/images/2fa/jupyter-jsc_2fa_img04.png title=\"2-factor-authentication\" width=\"320\" style=\"float:left\"/>\n",
+ " <!-- <img src=images/jupyter-jsc_2fa_img04.png title=\"2-factor-authentication\" width=\"320\" style=\"float:left\"/> -->\n",
+ "</div>\n",
+ "<div>\n",
+ " <!-- <img src=https://jupyter-jsc.fz-juelich.de/hub/static/default/images/2fa/jupyter-jsc_2fa_img04-1.png title=\"2-factor-authentication\" width=\"320\" style=\"float:right\"/>-->\n",
+ " <img src=https://raw.githubusercontent.com/FZJ-JSC/jupyter-jsc-notebooks/master/001-Jupyter/images/jupyter-jsc_2fa_img04-1.png title=\"2-factor-authentication\" width=\"120\" style=\"float:right\"/>\n",
+ "</div>\n",
+ "\n",
+ "### a. OTP-App Installation\n",
+ "There are a large number of different *OTP-Apps* available that implemented the *TOTP algorithm*. \n",
+ "You have to install **one of them** - for example, take one of the following: \n",
+ "\n",
+ "Recommended, free & open-source:\n",
+ " - [**FreeOTP**](https://freeotp.github.io) ([iOS](https://apps.apple.com/de/app/freeotp-authenticator/id872559395), [Android](https://play.google.com/store/apps/details?id=org.fedorahosted.freeotp&hl=de)) \n",
+ " - [**KeeWeb**](https://keeweb.info) ([Windows](https://keeweb.info), [macOS](https://keeweb.info), [Linux](https://keeweb.info), [online](https://keeweb.info))\n",
+ "\n",
+ "Free, but closed source:\n",
+ " - [**Authy**](https://authy.com/download/) ([iOS](https://apps.apple.com/de/app/authy/id494168017), [Android](https://play.google.com/store/apps/details?id=com.authy.authy), [Windows](https://authy.com/download/), [macOS](https://authy.com/download/), [Linux](https://snapcraft.io/authy)) \n",
+ " - [**Protectimus Smart OTP**](https://www.protectimus.com/protectimus-smart) ([iOS](https://apps.apple.com/ie/app/protectimus-smart/id854508919), [Android](https://play.google.com/store/apps/details?id=com.protectimus.android)) \n",
+ " - [**Google Authenticator**](https://de.wikipedia.org/wiki/Google_Authenticator) ([iOS](https://apps.apple.com/de/app/google-authenticator/id388497605), [Android](https://play.google.com/store/apps/details?id=com.google.android.apps.authenticator2) ) \n",
+ " - [**Microsoft Authenticator**](https://www.microsoft.com/en-us/account/authenticator) ([iOS](https://apps.apple.com/de/app/microsoft-authenticator/id983156458), [Android](https://play.google.com/store/apps/details?id=com.azure.authenticator), [Windows 10 Mobile](https://www.microsoft.com/en-us/p/microsoft-authenticator/9nblgggzmcj6)) \n",
+ "\n",
+ "The *TOTP algorithm* can also be implemented in hardware as a so-called \"hardware token\" (e.g. [Protectimus Tokens](https://www.protectimus.com/tokens/), [Microcosm Tokens](https://www.microcosm.com/products/oath-otp-authentication-tokens)) \n",
+ " \n",
+ "<div style=\"clear:both\"></div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ " <img src=https://jupyter-jsc.fz-juelich.de/hub/static/default/images/2fa/jupyter-jsc_2fa_img05.png title=\"2-factor-authentication\" width=\"320\" style=\"float:left\"/>\n",
+ " <!-- <img src=images/jupyter-jsc_2fa_img05.png title=\"2-factor-authentication\" width=\"320\" style=\"float:left\"/> -->\n",
+ "</div>\n",
+ "\n",
+ "### b. OTP-App Initialization & Validation\n",
+ "Before you can use 2FA for Jupyter-JSC a random, user-specific, unique and **secret initialization code** must be known by both Jupyter-JSC and the your *OTP-App*.\n",
+ "This *secret initialization code* gets generated by Jupyter-JSC and is shown as a **QR-Code** (or string) on the activation page.\n",
+ "\n",
+ "The QR-Code provides the *secret initialization code* with the descriptive data (1) algorithm = TOTP, (2) period of validity = 30s.\n",
+ "**If you prefer to use the string** instead of the QR-Code, please ensure you set these descriptive dates manually in your *OTP-App*.\n",
+ "\n",
+ "Next, the *OTP-App* provides now a **verification code** you have to enter on the activation webpage.\n",
+ "Jupyter-JSC compares the *verification code* you provide with the one generated by Jupyter-JSC.\n",
+ "\n",
+ "If they match, **2FA is now activated**.\n",
+ "\n",
+ "<div style=\"clear:both\"></div>\n",
+ "\n",
+ "----------------------"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ " <img src=https://jupyter-jsc.fz-juelich.de/hub/static/default/images/2fa/jupyter-jsc_2fa_img06.png title=\"2-factor-authentication\" width=\"320\" style=\"float:right\"/>\n",
+ " <!-- <img src=images/jupyter-jsc_2fa_img06.png title=\"2-factor-authentication\" width=\"320\" style=\"float:right\"/> -->\n",
+ "</div>\n",
+ "\n",
+ "### 2FA-Login at Jupyter-JSC\n",
+ "Congratulation! You are now ready to use 2-Factor Authentication with Jupyter-JSC.\n",
+ "\n",
+ "Login is now as simple as this\n",
+ "1. **Enter your JSC-account password** \n",
+ " Each time you log in, you enter your JSC-account password as usual. \n",
+ "2. **Enter the current one-time password** \n",
+ " You will then be asked for a *one-time password* that you can read from your installed & initialized *OTP-App* (e.g. on your smartphone). \n",
+ " \n",
+ "**Remember me** \n",
+ "Jupyter-JSC can set a cookie to remember, that you have logged in from this device already. \n",
+ "Just check the \"Remember me\" **checkbox** where you enter *one-time password* . \n",
+ "Jupyter-JSC **skips the request** of a *one-time password* in this browser on that device then for **one week**. \n",
+ " \n",
+ " "
+ ]
+ }
+ ],
+ "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": "3.8.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/02-Configuration/details/HDFCloud.ipynb b/02-Configuration/details/HDFCloud.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d7e639f08879d94beaad4b1052ce155c7ddbe734
--- /dev/null
+++ b/02-Configuration/details/HDFCloud.ipynb
@@ -0,0 +1,182 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "362c3138-d0bd-4dd2-8d5b-21339bb5244d.png": {
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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "\n",
+ "<h5 style=\"text-align: right\">Author: <a href=\"mailto:t.kreuzer@fz-juelich.de?subject=Jupyter-JSC%20documentation\">Tim Kreuzer</a></h5> \n",
+ "<h5><a href=\"../../index.ipynb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">HDF-Cloud on Jupyter-JSC</h1> "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Where is this JupyterLab running?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This JupyterLab instance is running on a [virtual machine](https://www.techopedia.com/definition/4805/virtual-machine-vm) on the [HDF-Cloud](https://www.fz-juelich.de/ias/jsc/EN/Expertise/SciCloudServices/HDFCloud/_node.html). It is started as a [Docker Container](https://www.docker.com/resources/what-container)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## How do I stop this JupyterLab?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can stop this JupyterLab in the Control Panel of Jupyter-JSC. \n",
+ "You can reach the Control Panel [here](https://jupyter-jsc.fz-juelich.de/hub/start) or in the menu File -> Hub Control Panel. \n",
+ "The JupyterLabs will be terminated after 30 days, so make sure your data is backed up."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## How can I upload files to JupyterLab?"
+ ]
+ },
+ {
+ "attachments": {
+ "1c47754c-50b1-4af2-a84d-dedbb31bf6d5.png": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVkAAABCCAYAAAD9lKU3AAAABHNCSVQICAgIfAhkiAAAEp9JREFUeJzt3XtQVOfdwPEv7HJbCAEFvAAbIBkxrzRdqTEKUrlMUm1iDL5egrVBSvqmqBSNRXPrGGI0DWRUgrHNpAvqREFezVDlTWxz0wramA5sWk3XoegGFBBWQ7jsBljl/YPuiSv3ssvu2uczszPynMPuj+Nzfvuc3/PsWZfe3t5eBEEQBJtwtXcAgiAIdzK5LZ60oKAAgPT0dBQKhS1eQhAEwSmMaSTb29mK6ZLGoq2uro7q6mqqq6upq6sbU3CCIAjOzmW4muzBgwe5du0amZmZAHSfO4Hh0318+1kZvYZWaT95mAqfRevxSkilqqoKvV7PI488YtvoBUEQHNyw5QKFQoHRaASgTb2ezv/LH3A/k05Da8FqOo7t4oHMIuTRIsEKgiAMO5I1a31zNcYT+5AF3oP7jHhkQWF4RMUD0HNJw83OVrrPn6D7/ElcFH4EbP0UebjKlrELgiA4vBEl2c5ju/j2bBl3rXgZ938l1sH0drbS8a/9A3dohtxXEAThTjdsku3tbMXwyV68F60f1RObLmkwNevwfOiJMQX4n+jy5cuEhITYOwxBGBPRj/sMu7rAxdtv1AkWQB6uksoJ1qJSqYiIiOj3OHbsGDqdjoiICAD279/P6tWrrfraw7n//vsHjC0lJWXA/Y8dO8bixYsBOHTokLSfTqeT2q2loKCAH//4xwNu27hxI88//7xFDI7kpz/9qcXxvO+++5g9ezYvvPAC3d3d9g4PgM7OTiIiIvjyyy+ltu7ubjIyMliwYAFXr14dt1hs2ffLy8tZsWIFKpWK+++/n8cee4wDBw5I223dj4uKigY8xyIiIlCpBi9N1tbWSrnBHmyyTtbMxdvP6s+Zl5dHTEyMRZu/vz83b97krbfesvrrjUZ+fj6zZs2yaHN3dx/292JjYwkLCwPAYDDQ1dVl1biWL1/Om2++yfnz55kxY4bU3t7ezvHjxykpKcHf31+KwdFkZGSwZs0aAG7cuMG5c+fIysoiODiYtWvX2jm6/rq6uli7di16vZ7i4mL8/f3tHdKY5efnU1RUxIYNG9i6dSsKhQKNRsOrr75Ke3s7v/jFL8alHy9YsACADz74gL1793Lo0CEAXF0d93NVjhvZIPz9/ZkyZYrFw9PTk+bm5kFPuPLycn70ox+hUqlYtWoVly5dsklsEyZM6BfbxIkTgb7RzsaNG/n+979PYmIi1dXV0u9VVlaya9cubty4werVqzEajcTExNDS0mKVuCZNmkRCQgLvvfeeRXt5eTn33nsv3/ve96QYbt020DFbuHAhR48eBfpOpMjISA4ePAhAT08PDzzwAPX19VaJ28zd3R1vb2+8vb3x9fUlJiaG+Ph4zp8/D/QfvbW0tBAREUFnZyc6nY45c+awc+dOHn/8cWJjY8nLy7NqfLcyGo08/fTTGAwG3n33XYsEO9gxra2tJS4ujg0bNqBSqfj444+JiYnh7bffJjk5mdjYWDIyMujs7ATAZDKxc+dO4uLimD17Nps3b6a9vd1mf9Ply5cpKChgz549pKamMm3aNEJCQnjsscfIzc3FxcUFsH0/9vb2ls4rX19fZDKZ9HNAQAC5ubkkJSURFRVFXFxcv/5eUFDA7NmzSUxMpLS0VGo/fPgw8fHxqFQqlixZwmeffWaVeM2cLsmO1ueff87zzz/Pq6++yueff05SUhLp6en09PSMaxyvvfYaTU1NnDp1iv3793Py5Ml++8hkMvbu3YuXlxenT58mMDDQaq+fkpLC0aNHMZlMUltpaSkrV67st+9QxywxMZHKykppP5lMxl/+8hfp5+DgYEJDQ60W90C+/PJL/vznPw95iXir5uZm5HI5R48e5cCBAxQWFqLVaq0eV2dnJ2lpaVy/fp2ioiJ8fHykbcP1wytXrqBUKnn//feZM2cOAGfPnuXQoUN8/PHH6HQ6Dh8+DMBvf/tbPvzwQ0pKSvj0008xGo3k5ORY/e8x++ijj7jnnnv6XUEC/PCHP+SZZ56xaLNlPx7MkSNH+OMf/0hJSQl///vfyczM5OWXX7bo71euXOHUqVPk5uaSk5NDVVUV7e3tvPTSS6jVajQaDYsXL+bXv/61VWNzuiS7du1aoqKipMezzz475P6lpaUkJyfz4IMP4ubmRlpaGiaTSUoM1vT0009bxBYVFSW9Y5aXl7N27Vp8fX0JCQkhPT3d6q8/lPnz5+Pp6Skl95qaGi5evMjjjz/eb9+hjllCQoKUZM+cOcPy5culd/4TJ06QmJho9dh/97vfoVKpUKlUzJgxg9TUVJKTk0lLSxvxc/zkJz8BICwsjLCwML766iurx7lhwwbc3d2pra3liy++sNg2kn64atUqpk6dire3NwBLly7F3d0dT09PHnroIXQ6nfRc69atIzg4GG9vbzZv3kxZWZnVL8/NmpubmTx5skWbecRofhgMBpu89kgtXLiQAwcOEBgYSEtLC15eXnR0dEhr/F1dXXnxxRfx8PBg1qxZPProo5SXl+Pu7o6bmxvFxcV88cUXrFq1ij/96U9WjW3YmmxvZ+u/XVsdy+8OZsuWLdI7PSB1yME0NDRQVVUlXeJC36REQ0ODVeMC2LZtG9HR0RZtEydOxGg00tbWZtFRx3vW1dXVlWXLlnHkyBGSkpIoLS1l8eLFA95bYqhjtnTpUgwGA5cuXeL06dPk5eVx/PhxamtrOXnyJK+99prVY1+5ciVpaWm0tbWxY8cOTCYTmZmZuLm5jfg5JkyYIP3b1dWVmzdvWj3OxMREcnJyeOONN8jKyqK8vFwqFw11TKdOnYqrq2u/Ed+tpQaZTIbJZKK3t5fGxkY2b97MCy+8IG13c3OzSZ+Gvj7c2Nho0bZv3z5MJhP19fWkpqZi75v59fT0sG3bNioqKpg0aRLTpk0DkOLy9/fnrrvukvYPDg6mpqYGDw8P3n33XXbv3s2qVavw8vIiIyNjVG/gwxk2ybp4+9GmXo9v+q7hdrVgqyVcQUFBo5qgCQoKIj09nV/96ldSm06nY9KkSVaNC/pqn4PF5uvrS0NDgzTLOdiMs7m+ZQvLly8nKSmJa9eu8Yc//IH9+/cPuN9Qx0wmkzF//nw++OADmpqamDZtGnPnzuW9997j+vXrI76EHw3z6B9gz549rFixgjVr1lBUVISrqysymcxipcH169etHsNIPPnkk7i4uLBhwwbOnDnD+vXr2bdvH66urkMe04aGhhH/v7u4uBAYGEheXh7z5s0D+hJMfX09SqXSJn9XUlIS27dv59SpU8TFxQHfDRIGK7vZsh8PZPv27Xz77becPn0aLy8vLl68SHl5ubS9o6ODnp4e6Y25paWF4OBg2tra6O7u5p133qGnp4eTJ0+SkZFBTEwMkZGRVoltROUCt3AVzc+EY/x037D73mjW0VqQxjeF6x1ijeySJUs4ePAgGo2G3t5ePvroIxYsWEBTU5PVX+v69es0NjZaPMyvs3TpUvLz87l27RqNjY38/ve/H/A53N3d6e7u5uLFi9y4ccOq8U2ePJmYmBheeeUVQkNDmT59+oD7DXfMEhISUKvVzJo1CxcXF+bOncvevXtJSEiw+Syvu7s7O3fu5K9//SuFhYUAhIeHo9Fo0Gg0fP311+zZs2fcT/JbyeVy8vPz+dvf/ibdkc6a/XDJkiXs2LGDxsZGenp62LVrF2lpaTYbTYaFhZGVlcWaNWt46623OHfuHDU1NZSWlrJmzRqUSmW/qwpb9uOBtLW1cffdd+Pp6UlHR4c0iWt+E+jq6pIGFfX19Rw/fpwlS5bQ3t7OU089xZkzZ3BzcyMgIACZTGYx6h2rES3h8kpcTde5E7QWrOYb9Xo8ouJx+9dHZl28/ejtbOVmZyvfflbGjRYdLoq7mfS2zmpBjkVcXBwvvvgimzZtorGxkZCQEHbv3k14eLjVXysrK6tfm4eHB//4xz/Izs7mlVdeISkpCR8fHxYuXMjZs2f77a9UKqWaUVlZmdXeTc1WrlxJenr6kDPswx2z+fPn09bWJpVt5s6di9FoJCkpyaqxDiYsLIznnnuO7du3Ex8fz9y5c1m2bBk///nPcXV1ZePGjVavq41WaGgoW7du5dlnn+UHP/jBkMe0trZ2VM+dlZVFb28vy5Yto729naioKNRqNXK57VZk/vKXv2TGjBns27cPtVqN0WgkLCyM5ORkUlNT+y1VtHU/vt3GjRvJzs7mwQcfxMfHh+TkZIKCgqipqSEwMBB/f3+++uorFi1axM2bN9m2bZsU0+uvv85LL73E1atXCQwMJDc3l6lTp1otthHfuwCgveRlOkqHnsV0nzGfCc+V2WSNrCAIgrMZUZLVarXU19cTGxuLR0ezdC+DGy19M7QuirvxiIrHc/YTeCWutnXMgiAITmPYJKvX69m0aRPQ98mk8V56JAiC4MyGnaW4df2bvdfCCYIgOJsRlQuqqqqoq6vjkUceEd/ZJQiCMAqjmvgSBEEQRsfpPlYrCILgTESSFQRBsCH5J598Yu8YBEEQ7liiJisIgmBDolwgCIJgQzb9+hlB+LfY8eYuFsRFnmAFYiQrOBZHSbCCYCViJCs4JnuOIkWiF6xIjGQFQRBsSCRZQRAEGxJJVhAEwYacsiZ7uraT9f9bz+Wvx/drvR1diL8bu5aFEnPv0F8uKQjC+HHKDyPM/o1WJNhBhPi7cfa5gb+7yymYJ50cYeLL+U4NwQE5ZblAJNjBiWMjCI7FKZOsIAiCsxBJVhAEwYZEkhUEQbAhkWQFQRixrq4uurq67B2GU3HKJVyCbWm1Wlxu+2hpZGSknaIZnYqKCi5cuODQ36qs1+u5du0aABMnTiQgIMDOEY1cc3MzAKGhoXaOxHmIJDtGOYumALDlWKOdI7GOsrIyjh492q/dGb4OvqKigsLCQulnR4vXYDCgVquprq62aJ85cybp6elUVVUREBDA9OmOsQTv8uXLTJ48GblcTldXF83NzbS2tkrbg4KC8PDwsGOEzkEk2TGKmupl7xCs6sKFCwO2V1ZWUllZOeC2devWER0dbcuwhnV7gjXH6iiJ1mAwkJ2djUKhYN26dSiVSgICAqiqqqK4uJjs7GyMRiObNm2yd6gA1NTUcOXKFZqamlCpVPYOx6mJmqwwZoMl3/Fye4I1q6ysRK1W2yGi/tRqNQqFgpycHKKjo6USQXR0NA8//DBGo9HOEX6nqamJKVOmIJfL6ejoQKPRIJPJCA0Nxc/PDz8/P0JDQ8UodoSGHclWVFQMeBIplUpSUlJsEpTguMwjrdzcXKnNYDDYKxzq6uqorKwkMjKy3yg8MjISvV5PRUUF8+bNs1OEfcenurqadevWoVAoLLZVVFRQUlJip8j602q1NDU1ERwczJw5c9Bqtej1ejQaDSqViqCgIHuH6HSGTLJ1dXUDjhCg77LSYDA4zOXYeMlZNIUZU74rEfzXFE8ADv9PhNR2vtF4x9RoHZ1SqWTz5s0A/OxnP7PYZm63t7q6OoABa63z5s2z6xvArcwJFuDq1auEh4cTFhZGa2urNKJVqVTI5aLKOBpDHq3hRihD1emgbyThKB3dmm6deDfPwt/p93netGmTRZIoLCxEq9VajGgF53VrggUwmUx0dHRINVmdTmcxohWJduRseqQuXLiAVqt1mNlSa7h9hHrkmb4R7H+/fdEe4Yyb4uJiFAqFVCIqLi62a5nAmSiVSqAvkQ02QWiuK+/evbtfSWE86PX6AdubmpoICAjgvvvuQ6/X09HRQWtrq1MtO7M38XYkjEh9fT3w3dXNYKsQ7MlR1/IqFApmzpxJcXEx06dP75dEDQYDH374ITNnzrRLggVQqVRoNBpMJlO/bf/85z+lf0+ePFkk2FESSVYYleLiYnuHMChHLk2lp6eTnZ3Nli1bWLlyJaGhoSgUCqqrqykrK0OhUNh1fsPHx8ci0crlcnx8fAgODubKlStAX4K9k65Kx4tIsmN0rsFxlt5Yw0Cz9Lcyj2hv/x17ef3114cdVTvC3IBCoSAvLw+1Wk1BQYHFttjYWFJSUuw2ijW7PdHq9Xq++eYbQCTYsRBJdozutFUETzzxxKhPJnuefCkpKcPWhu2dvMwUCgWZmZno9XqpBqpUKh0mPrBMtFqtFhAJdqxEkhX6caYTyjyp5EwCAgIcuq55a6J1pI/5OiuRZAVB6MfHx8dh1u86O/GxWkEQBBtyyiR7l6dThj0ugv3c7B2CIAi3GDJbWaMgb4uiftFTYSKZDCDYz4385eI+n4LgSIb9SvDBbhAzEg8//LDdb4EnOBlH+DpuR4hBuGMMm2QFYVw5QoJzhBiEO4ZYXSA4pjv9jjvCfwwxgyQIgmBDYiQrOBZxiS7cYcRIVhAEwYZEkhUEQbCh/wdeBouTrestBwAAAABJRU5ErkJggg=="
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can use git or you upload files with this button in the top left corner: \n",
+ " \n",
+ "![Upload files](attachment:1c47754c-50b1-4af2-a84d-dedbb31bf6d5.png \"Upload Files\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## How can I download a file from JupyterLab?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Just right click on a file and click \"Download\"."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## How much memory do I have?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You will see your resource quotas during the startup process of your JupyterLab. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## How much disk space do I have?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can store 25 GB of data in `/home/jovyan`. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Collaborative work\n",
+ "We offer three different solutions to share your work with your colleagues. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### GIT\n",
+ "You can use the git command in a terminal to work on any git repositories. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### B2DROP"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can easily mount your [B2DROP](https://b2drop.eudat.eu) files into this JupyterLab. \n",
+ "Just run the command `mount B2DROP` in a terminal and insert your [application credentials](https://eudat.eu/services/userdoc/b2drop#UserDocumentation-B2DROPUsage-WebDavclient)\\. \n",
+ "If you want to store your [application credentials](https://eudat.eu/services/userdoc/b2drop#UserDocumentation-B2DROPUsage-WebDavclient) add the following line to `/home/jovyan/work/.davfs2/secrets`: \n",
+ "```https://b2drop.eudat.eu/remote.php/webdav \\<USERNAME> \\<PASSWORD>``` \n",
+ "To unmount it run `umount B2DROP`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### JUST\n",
+ "We use the [UFTP Service](https://apps.fz-juelich.de/jsc/hps/judac/uftp.html) to mount your files from the HPC Systems. This should be done automatically during the start process. You will find your `$HOME` directory at `/home/jovyan/JUST_HOMEs_readonly`. Currently, we mount it as readonly, because we're still testing this tool on Jupyter-JSC and don't want to risk any data loss. "
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.8.10 64-bit",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.10"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/02-Configuration/details/List_PythonPackages.ipynb b/02-Configuration/details/List_PythonPackages.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f1d56fdddad8504c144ee1c3f900fc870452da44
--- /dev/null
+++ b/02-Configuration/details/List_PythonPackages.ipynb
@@ -0,0 +1,434 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "header.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "<h5 style=\"text-align: right\">Author: <a href=\"mailto:j.goebbert@fz-juelich.de?subject=Jupyter-JSC%20documentation\">Jens Henrik Göbbert</a></h5> \n",
+ "<h5><a href=\"../../index.ipynb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">Kernels & Proxies</h1> "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "## List of included Python packages\n",
+ "-------------------------"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This lists the python packages available and installed by the install script for Python, SciPy-Stack and Jupyter:\n",
+ " - `$EBROOTPYTHON/easybuild/*.eb`\n",
+ " - `$EBROOTSCIPYMINSTACK/easybuild/*.eb`\n",
+ " - `$EBROOTJUPYTER/easybuild/*.eb`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!ls $EBROOTJUPYTER/lib/python3.6/site-packages/ | grep .dist-info > ${PKG_DISTINFO}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pkg_list = [\n",
+ " # Python module\n",
+ " (\"setuptools\", \"41.6.0\", \"\"),\n",
+ " (\"webencodings\", \"0.5.1\", \"\"),\n",
+ " (\"six\", \"1.12.0\", \"\"),\n",
+ " (\"decorator\", \"4.4.0\", \"\"),\n",
+ " (\"MarkupSafe\", \"1.1.1\", \"markupsafe\"),\n",
+ " (\"more-itertools\", \"7.2.0\", \"more_itertools\"),\n",
+ " (\"pickleshare\", \"0.7.5\", \"\"),\n",
+ " (\"jedi\", \"0.15.1\", \"\"),\n",
+ " (\"wcwidth\", \"0.1.7\", \"\"),\n",
+ " (\"attr\", \"19.3.0\", \"\"),\n",
+ " (\"parso\", \"0.5.1\", \"\"),\n",
+ " (\"jinja2\", \"2.10.1\", \"\"),\n",
+ " (\"pytz\", \"2019.3\", \"\"),\n",
+ " (\"pyparsing\", \"2.2.0\", \"\"),\n",
+ " (\"packaging\", \"19.2\", \"\"),\n",
+ " (\"urllib3\", \"1.25.6\", \"\"),\n",
+ " (\"certifi\", \"2019.9.11\", \"\"),\n",
+ " (\"requests\", \"2.22.0\", \"\"),\n",
+ " (\"python-dateutil\", \"2.8.1\", \"dateutil\"),\n",
+ " (\"Pillow\", \"6.2.1\", \"PIL\"),\n",
+ " (\"ply\", \"3.11\", \"\"),\n",
+ " (\"pyrsistent\", \"0.15.4\", \"\"),\n",
+ " (\"lxml\", \"4.4.1\", \"\"),\n",
+ " (\"idna\", \"2.8\", \"\"),\n",
+ " (\"chardet\", \"3.0.4\", \"\"),\n",
+ " (\"pycparser\", \"2.19\", \"\"),\n",
+ " (\"cffi\", \"1.13.2\", \"\"),\n",
+ " (\"psutil\", \"5.6.3\", \"\"),\n",
+ " (\"SQLAlchemy\", \"1.3.10\", \"sqlalchemy\"),\n",
+ " (\"certipy\", \"0.1.3\", \"\"),\n",
+ " (\"python-editor\", \"1.0.4\", \"editor\"),\n",
+ " (\"Mako\", \"1.1.0\", \"mako\"),\n",
+ " (\"alembic\", \"1.2.1\", \"\"),\n",
+ " (\"click\", \"7.0\", \"\"),\n",
+ " (\"appdirs\", \"1.4.3\", \"\"),\n",
+ " (\"cloudpickle\", \"1.2.2\", \"\"),\n",
+ " (\"toolz\", \"0.10.0\", \"\"),\n",
+ " (\"cryptography\", \"2.8\", \"\"),\n",
+ " \n",
+ " (\"prompt-toolkit\", \"2.0.10\", \"prompt_toolkit\"),\n",
+ " (\"oauthlib\", \"3.1.0\", \"\"),\n",
+ " (\"async-generator\", \"1.10\", \"async_generator\"),\n",
+ " (\"smmap\", \"0.9.0\", \"\"),\n",
+ " (\"typed-ast\", \"1.4.0\", \"typed_ast\"),\n",
+ "\n",
+ " # SciPy-Stack module\n",
+ " (\"cycler\", \"0.10.0\", \"\"),\n",
+ " (\"numpy\", \"1.15.2\", \"\"),\n",
+ " (\"scipy\", \"1.2.1\", \"\"),\n",
+ " (\"sympy\", \"1.4\", \"\"),\n",
+ " (\"pandas\", \"0.25.3\", \"\"),\n",
+ " (\"mpmath\", \"1.1.0\", \"\"),\n",
+ " (\"kiwisolver\", \"1.1.0\", \"\"),\n",
+ " (\"backports.functools_lru_cache\", \"1.5\", \"\"),\n",
+ " (\"matplotlib\", \"3.1.1\", \"\"),\n",
+ " (\"xarray\", \"0.11.3\", \"\"),\n",
+ " \n",
+ " # Jupyter module\n",
+ " (\"pyOpenSSL\", \"19.0.0\", \"OpenSSL\"),\n",
+ " (\"entrypoints\", \"0.3\", \"\"),\n",
+ " (\"async_generator\", \"1.10\", \"\"),\n",
+ " (\"absl-py\", \"0.8.1\", \"absl\"),\n",
+ " (\"cryptography\", \"2.8\", \"\"),\n",
+ " (\"tornado\", \"6.0.3\", \"\"),\n",
+ " (\"bokeh\", \"1.3.4\", \"\"),\n",
+ " (\"seaborn\", \"0.9.0\", \"\"),\n",
+ " (\"nbformat\", \"4.4.0\", \"\"),\n",
+ " (\"param\", \"1.9.2\", \"\"),\n",
+ " (\"pyviz_comms\", \"0.7.2\", \"\"),\n",
+ " (\"holoviews\", \"1.12.6\", \"\"),\n",
+ " (\"alabaster\", \"0.7.12\", \"\"),\n",
+ " (\"Babel\", \"2.7.0\", \"babel\"),\n",
+ " (\"snowballstemmer\", \"2.0.0\", \"\"),\n",
+ " (\"docutils\", \"0.15.2\", \"\"),\n",
+ " (\"imagesize\", \"1.1.0\", \"\"),\n",
+ " (\"sphinxcontrib-websupport\", \"1.1.2\", \"sphinxcontrib.websupport\"),\n",
+ " (\"Sphinx\", \"1.8.5\", \"sphinx\"),\n",
+ " (\"pexpect\", \"4.7.0\", \"\"),\n",
+ " (\"ipython\", \"7.9.0\", \"IPython\"),\n",
+ " (\"ipynb\", \"0.5.1\", \"\"),\n",
+ " (\"jupyter_core\", \"4.6.1\", \"\"),\n",
+ " (\"retrying\", \"1.3.3\", \"\"),\n",
+ " (\"plotly\", \"4.2.1\", \"\"),\n",
+ " (\"tikzplotlib\", \"0.8.4\", \"\"),\n",
+ " (\"jupyter_client\", \"5.3.4\", \"\"),\n",
+ " (\"traitlets\", \"4.3.3\", \"\"),\n",
+ " (\"pyzmq\", \"18.1.0\", \"zmq\"),\n",
+ " (\"singledispatch\", \"3.4.0.3\", \"\"),\n",
+ " (\"ipyparallel\", \"6.2.4\", \"\"),\n",
+ " (\"ipykernel\", \"5.1.3\", \"\"),\n",
+ " (\"terminado\", \"0.8.2\", \"\"),\n",
+ " (\"bleach\", \"3.1.0\", \"\"),\n",
+ " (\"mistune\", \"0.8.4\", \"\"),\n",
+ " (\"pandocfilters\", \"1.4.2\", \"\"),\n",
+ " (\"Pygments\", \"2.4.2\", \"pygments\"),\n",
+ " (\"testpath\", \"0.4.4\", \"\"),\n",
+ " (\"nbconvert\", \"5.6.1\", \"\"),\n",
+ " (\"ipython_genutils\",\"0.2.0\", \"\"),\n",
+ " (\"Send2Trash\", \"1.5.0\", \"send2trash\"),\n",
+ " (\"notebook\", \"6.0.2\", \"\"),\n",
+ " (\"version_information\", \"1.0.3\", \"\"),\n",
+ " (\"lesscpy\", \"0.13.0\", \"\"),\n",
+ " (\"prometheus-client\", \"0.7.1\", \"prometheus_client\"),\n",
+ " (\"jupyterthemes\", \"0.20.0\", \"\"),\n",
+ " (\"zipp\", \"0.6.0\", \"\"),\n",
+ " (\"importlib_metadata\", \"0.23\", \"\"),\n",
+ " (\"jsonschema\", \"3.1.1\", \"\"),\n",
+ " (\"jupyterlab_launcher\", \"0.13.1\",\"\"),\n",
+ " (\"sphinx_rtd_theme\",\"0.4.3\", \"\"),\n",
+ " (\"future\", \"0.18.1\", \"\"),\n",
+ " (\"commonmark\", \"0.9.1\", \"\"),\n",
+ " (\"recommonmark\", \"0.6.0\", \"\"),\n",
+ " (\"jupyterlab\", \"1.2.1\", \"\"),\n",
+ " (\"json5\", \"0.8.5\", \"\"),\n",
+ " (\"jupyterlab_server\", \"1.0.6\", \"\"),\n",
+ " (\"ptyprocess\", \"0.6.0\", \"\"),\n",
+ " (\"defusedxml\", \"0.6.0\", \"\"),\n",
+ " (\"widgetsnbextension\", \"3.5.1\", \"\"),\n",
+ " (\"ipywidgets\", \"7.5.1\", \"\"),\n",
+ " (\"ipydatawidgets\", \"4.0.1\", \"\"),\n",
+ " (\"traittypes\", \"0.2.1\", \"\"),\n",
+ " (\"bqplot\", \"0.11.9\", \"\"),\n",
+ " (\"jupyter_bokeh\", \"1.1.1\", \"\"),\n",
+ " (\"pythreejs\", \"2.1.1\", \"\"),\n",
+ " (\"PyWavelets\", \"1.1.1\", \"pywt\"),\n",
+ " (\"imageio\", \"2.6.1\", \"\"),\n",
+ " (\"networkx\", \"2.3\", \"\"),\n",
+ " (\"scikit-image\", \"0.16.2\", \"skimage\"),\n",
+ " (\"ipywebrtc\", \"0.5.0\", \"\"),\n",
+ " (\"ipyvolume\", \"0.5.2\", \"\"),\n",
+ " (\"branca\", \"0.3.1\", \"\"),\n",
+ " (\"ipyleaflet\", \"0.11.4\", \"\"),\n",
+ " (\"ipympl\", \"0.3.3\", \"\"),\n",
+ " (\"PyYAML\", \"5.1.2\", \"yaml\"),\n",
+ " (\"jupyter_nbextensions_configurator\", \"0.4.1\", \"\"),\n",
+ " (\"jupyter_latex_envs\", \"1.4.6\", \"latex_envs\"),\n",
+ " (\"jupyter_highlight_selected_word\", \"0.2.0\", \"\"),\n",
+ " (\"jupyter_contrib_core\", \"0.3.3\",\"\"),\n",
+ " (\"jupyter_contrib_nbextensions\", \"0.5.1\", \"\"),\n",
+ " (\"rise\", \"5.5.1\", \"\"),\n",
+ " (\"typing-extensions\", \"3.7.4\", \"typing_extensions\"),\n",
+ " (\"idna-ssl\", \"1.1.0\", \"idna_ssl\"),\n",
+ " (\"multidict\", \"4.5.2\", \"\"),\n",
+ " (\"yarl\", \"1.3.0\", \"\"),\n",
+ " (\"async-timeout\", \"3.0.1\", \"async_timeout\"),\n",
+ " (\"aiohttp\", \"3.6.2\", \"\"),\n",
+ " (\"simpervisor\", \"0.3\", \"\"),\n",
+ " (\"jupyter_server\", \"0.1.1\", \"\"),\n",
+ " (\"jupyter-server-proxy\", \"1.1.0\", \"jupyter_server_proxy\"),\n",
+ " (\"jupyterlab_github\", \"1.0.1\", \"\"),\n",
+ " (\"jupyterlab-gitlab\", \"0.3.0\", \"jupyterlab_gitlab\"),\n",
+ " (\"jupyterlab-quickopen\", \"0.3.0\", \"jupyterlab_quickopen\"),\n",
+ " (\"zstandard\", \"0.12.0\", \"\"),\n",
+ " (\"itk_core\", \"5.0.1\", \"\"),\n",
+ " (\"itk_filtering\", \"5.0.1\", \"\"),\n",
+ " (\"itk_segmentation\",\"5.0.1\", \"\"),\n",
+ " (\"itk_numerics\", \"5.0.1\", \"\"),\n",
+ " (\"itk_registration\",\"5.0.1\", \"\"),\n",
+ " (\"itk_io\", \"5.0.1\", \"\"),\n",
+ " (\"itk-meshtopolydata\", \"0.5.1\", \"\"),\n",
+ " (\"pyct\", \"0.4.6\", \"\"),\n",
+ " (\"colorcet\", \"2.0.2\", \"\"),\n",
+ " (\"itkwidgets\", \"0.22.0\", \"\"),\n",
+ " (\"ujson\", \"1.35\", \"\"),\n",
+ " (\"jupyterlab_iframe\", \"0.2.1\", \"\"),\n",
+ " (\"python-dotenv\", \"0.10.3\", \"dotenv\"),\n",
+ " (\"jupyterlab_latex\",\"1.0.0\", \"\"),\n",
+ " (\"jupyterlab_slurm\",\"1.0.5\", \"\"),\n",
+ " (\"jupyterlmod\", \"1.7.5\", \"\"),\n",
+ " (\"nbresuse\", \"0.3.2\", \"\"),\n",
+ " (\"colorama\", \"0.4.1\", \"\"),\n",
+ " (\"nbdime\", \"1.1.0\", \"\"),\n",
+ " (\"smmap2\", \"2.0.5\", \"smmap\"),\n",
+ " (\"gitdb2\", \"2.0.6\", \"gitdb\"),\n",
+ " (\"GitPython\", \"3.0.4\", \"git\"),\n",
+ " (\"jupyterlab-git\", \"0.8.1\", \"jupyterlab_git\"),\n",
+ " (\"sidecar\", \"0.3.0\", \"\"),\n",
+ " (\"pycodestyle\", \"2.5.0\", \"\"),\n",
+ " (\"autopep8\", \"1.4.4\", \"\"),\n",
+ " (\"yapf\", \"0.28.0\", \"\"),\n",
+ " (\"toml\", \"0.10.0\", \"\"),\n",
+ " (\"pathspec\", \"0.6.0\", \"\"),\n",
+ " (\"typed_ast\", \"1.4.0\", \"\"),\n",
+ " (\"regex\", \"2019.11.1\",\"\"),\n",
+ " (\"black\", \"19.3b0\", \"\"),\n",
+ " (\"jupyterlab-code-formatter\", \"0.6.1\", \"jupyterlab_code_formatter\"),\n",
+ " (\"pamela\", \"1.0.0\", \"\"),\n",
+ " (\"certipy\", \"0.1.3\", \"\"),\n",
+ " (\"oauthlib\", \"3.1.0\", \"\"),\n",
+ " (\"jupyterhub\", \"1.0.0\", \"\"),\n",
+ " (\"appmode\", \"0.6.0\", \"\"),\n",
+ " (\"HeapDict\", \"1.0.1\", \"heapdict\"),\n",
+ " (\"zict\", \"1.0.0\", \"\"),\n",
+ " (\"tblib\", \"1.5.0\", \"\"),\n",
+ " (\"sortedcontainers\",\"2.1.0\", \"\"),\n",
+ " (\"msgpack\", \"0.6.2\", \"\"),\n",
+ " (\"dask\", \"2.6.0\", \"\"),\n",
+ " (\"distributed\", \"2.6.0\", \"\"),\n",
+ " (\"dask-jobqueue\", \"0.7.0\", \"\"),\n",
+ " (\"dask_labextension\", \"1.0.3\", \"\"),\n",
+ " (\"Automat\", \"0.8.0\", \"automat\"),\n",
+ " (\"PyHamcrest\", \"1.9.0\", \"hamcrest\"),\n",
+ " (\"Twisted\", \"19.7.0\", \"twisted\"),\n",
+ " (\"autobahn\", \"19.10.1\", \"\"),\n",
+ " (\"constantly\", \"15.1.0\", \"\"),\n",
+ " (\"hyperlink\", \"19.0.0\", \"\"),\n",
+ " (\"incremental\", \"17.5.0\", \"\"),\n",
+ " (\"txaio\", \"18.8.1\", \"\"),\n",
+ " (\"zope.interface\", \"4.6.0\", \"\"),\n",
+ " (\"backcall\", \"0.1.0\", \"\"),\n",
+ " (\"wslink\", \"0.1.11\", \"\"),\n",
+ " (\"jupyterlab_pygments\", \"0.1.0\",\"\"),\n",
+ " (\"ipyvue\", \"1.0.0\", \"\"),\n",
+ " (\"ipyvuetify\", \"1.1.1\", \"\"),\n",
+ " (\"voila\", \"0.1.14\", \"\"),\n",
+ " (\"voila-material\", \"0.2.5\", \"-\"),\n",
+ " (\"voila-gridstack\", \"0.0.6\", \"-\"),\n",
+ " (\"voila-vuetify\", \"0.1.1\", \"-\"), \n",
+ " (\"dicom-upload\", \"v0.1.0\", \"\"),\n",
+ " (\"fileupload\", \"master\", \"\"),\n",
+ " (\"pvlink\", \"0.1.2\", \"\"),\n",
+ " (\"julia\", \"0.5.0\", \"\"),\n",
+ " (\"textwrap3\", \"0.9.2\", \"\"),\n",
+ " (\"ansiwrap\", \"0.8.4\", \"\"),\n",
+ " (\"backports.weakref\",\"1.0.post1\",\"\"),\n",
+ " (\"backports.tempfile\",\"1.0\", \"\"),\n",
+ " (\"tqdm\", \"4.41.0\", \"\"),\n",
+ " (\"tenacity\", \"6.0.0\", \"\"),\n",
+ " (\"papermill\", \"1.2.1\", \"\"),\n",
+ " \n",
+ " # PythonPackages for Tutorials\n",
+ " (\"patsy\", \"0.5.1\", \"\"),\n",
+ " (\"statsmodels\", \"0.10.2\", \"\"),\n",
+ " (\"cftime\", \"1.0.4.2\", \"\"),\n",
+ " (\"vega_datasets\", \"0.8.0\", \"\"),\n",
+ " (\"arviz\", \"0.5.1\", \"\"),\n",
+ " (\"Theano\", \"1.0.4\", \"\"),\n",
+ " (\"altair\", \"3.3.0\", \"\"),\n",
+ " (\"cssselect\", \"1.1.0\", \"\"),\n",
+ " (\"smopy\", \"0.0.7\", \"\"),\n",
+ " (\"joblib\", \"0.14.1\", \"\"),\n",
+ " (\"scikit-learn\", \"0.22\", \"\"),\n",
+ " (\"memory_profiler\", \"0.55.0\", \"\"),\n",
+ " (\"h5py\", \"2.10.0\", \"\"),\n",
+ " (\"line_profiler\", \"2.1.2\", \"\"),\n",
+ " (\"pymc3\", \"3.8\", \"\"),\n",
+ " (\"llvmlite\", \"0.30.0\", \"\"),\n",
+ " (\"numba\", \"0.46.0\", \"\"),\n",
+ " (\"numexpr\", \"2.7.0\", \"\"),\n",
+ " (\"ipythonblocks\", \"1.9.0\", \"\"),\n",
+ " (\"pydub\", \"0.23.1\", \"\"),\n",
+ " (\"multipledispatch\",\"0.6.0\", \"\"),\n",
+ " (\"partd\", \"1.1.0\", \"\"),\n",
+ " (\"locket\", \"0.2.0\", \"\"),\n",
+ " (\"fsspec\", \"0.6.2\", \"\"),\n",
+ " (\"datashape\", \"0.5.2\", \"\"),\n",
+ " (\"datashader\", \"0.9.0\", \"\"),\n",
+ " (\"selenium\", \"3.141.0\", \"\"),\n",
+ " (\"graphviz\", \"0.13.2\", \"\"),\n",
+ " (\"vincent\", \"0.4.4\", \"\"),\n",
+ " (\"Shapely\", \"1.6.4.post2\",\"\"),\n",
+ " (\"pyshp\", \"2.1.0\", \"\"),\n",
+ " (\"Cartopy\", \"0.17.0\", \"\"),\n",
+ " (\"pandas-datareader\",\"0.8.1\", \"\"),\n",
+ "]\n",
+ "\n",
+ "from pip._vendor import pkg_resources\n",
+ "def get_version(package):\n",
+ " package = package.lower()\n",
+ " return next((p.version for p in pkg_resources.working_set if p.project_name.lower() == package), f\"{Fore.RED}NO MATCH{Style.RESET_ALL}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Run a Sanity Check\n",
+ "A test of the Python packages follows here. \n",
+ "\n",
+ "Attention:\n",
+ " - Slight changes in the version numbers are due to compatibility problems we encountered and therefore had to make adjustments.\n",
+ " - \"NO MATCH\" - Not all package versions can be automatically found by this script\n",
+ " - \"IMPORT FAILED\" - Import failures are possible if python packages are not ment to be importable by the developer."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PYPI NAME : IMPORT NAME REQ.VERS.|INST.VERS. IMPORT TIME\n",
+ "=================================================================================\n"
+ ]
+ },
+ {
+ "ename": "NameError",
+ "evalue": "name 'pkg_list' 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-1-09245ef6496d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"PYPI NAME\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mljust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\": \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"IMPORT NAME\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mljust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"REQ.VERS.|INST.VERS.\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mljust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"IMPORT TIME\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"=================================================================================\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mpkg_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpkg_version\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpkg_importname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpkg_list\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mpkg_importname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mpkg_importname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpkg_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'pkg_list' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "import importlib\n",
+ "from colorama import Fore, Style\n",
+ "from timeit import default_timer as timer\n",
+ "\n",
+ "print(\"PYPI NAME\".ljust(20), \": \", \"IMPORT NAME\".ljust(20) + \"REQ.VERS.|INST.VERS.\".ljust(25) + \"IMPORT TIME\")\n",
+ "print(\"=================================================================================\")\n",
+ "for pkg_name, pkg_version, pkg_importname in pkg_list:\n",
+ " if not pkg_importname:\n",
+ " pkg_importname = pkg_name\n",
+ " pkg = None\n",
+ " \n",
+ " try:\n",
+ " # import package\n",
+ " start_time = timer()\n",
+ " if pkg_importname != \"-\":\n",
+ " pkg = importlib.import_module(pkg_importname)\n",
+ " import_time = timer() - start_time\n",
+ " \n",
+ " # get version\n",
+ " try:\n",
+ " version = pkg.__version__\n",
+ " if not isinstance(pkg.__version__, str):\n",
+ " raise\n",
+ " except:\n",
+ " version = get_version(pkg_name)\n",
+ " \n",
+ " if version != pkg_version:\n",
+ " version = pkg_version.ljust(10) + \" != \" + f\"{Fore.RED}\" + version.ljust(10) + f\"{Style.RESET_ALL}\"\n",
+ "\n",
+ " print(pkg_name.ljust(20), \": \", pkg_importname.ljust(20), version.ljust(24), f\"{import_time:.6f}\"+\"s\")\n",
+ " except:\n",
+ " print(pkg_name.ljust(20), \": \", pkg_importname.ljust(20), f\"{Fore.RED}IMPORT FAILED{Style.RESET_ALL}\") \n",
+ " \n",
+ " #try:\n",
+ " # print(\"\".ljust(24), pkg.__file__)\n",
+ " #except:\n",
+ " # print(\"\".ljust(24), f\"{Fore.RED}UNKNOWN{Style.RESET_ALL}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "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": "3.9.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/03-HowTos/Create_JupyterKernel_conda.ipynb b/03-HowTos/Create_JupyterKernel_conda.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..9560d7c77af00282c24da29eb26bc1380cc518b8
--- /dev/null
+++ b/03-HowTos/Create_JupyterKernel_conda.ipynb
@@ -0,0 +1,318 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "364f4d26-8fd6-45ed-a6c3-1ab27dad25c4.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "\n",
+ "<h5 style=\"text-align: right\">Author: <a href=\"mailto:s.luehrs@fz-juelich.de?subject=Jupyter-JSC%20documentation\">Sebastian Lührs</a></h5> \n",
+ "<h5><a href=\"../index.ipynb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">Create your own Jupyter CONDA-Kernel</h1> "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Often the standard kernel do not provide all features you need for your work. This might be that certain modules are not loaded or packages are not installed.\n",
+ "With your own kernel you can overcome that problem easily and define your own environment, in which you work.\n",
+ "\n",
+ "This notebook shows you how you can build your own kernel for a **conda environment**.\n",
+ "\n",
+ "--------------------------------------"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Building your own Jupyter CONDA-kernel is a three step process\n",
+ "Download Minconda installer\n",
+ "1. Download/Install Miniconda\n",
+ " * Miniconda3.sh\n",
+ "2. Create Conda Environment\n",
+ " * conda create\n",
+ "2. Create/Edit launch script for the Jupyter kernel\n",
+ " * kernel.sh\n",
+ "3. Create/Edit Jupyter kernel configuration\n",
+ " * kernel.json"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Settings"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Selectable **CONDA_TARGET_DIR** path for the central conda installation, should be in the project filesystem"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "export CONDA_TARGET_DIR=${HOME}/PROJECT_training2005/testdir/miniconda3"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Selectable **CONDA_ENV** name, will be used to specify the environment name\n",
+ " - must be lowercase"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "CONDA_ENV=my_env\n",
+ "\n",
+ "export CONDA_ENV=$(echo \"${CONDA_ENV}\" | awk '{print tolower($0)}')\n",
+ "echo ${CONDA_ENV} # double check"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "## 1. Download/Install Miniconda"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Start here if you want to run the full installation.\n",
+ "If you want to create another environment in an existing conda setup go to **create environment**. If you want to attach yourself to an existing environment go to **create user kernel**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* 1.1 - Download Minconda installer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "wget --output-document=$HOME/Miniconda3.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* 1.2 - Create target directory"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mkdir -p ${CONDA_TARGET_DIR}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* 1.3 - Install Miniconda"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bash $HOME/Miniconda3.sh -b -u -p ${CONDA_TARGET_DIR}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "${CONDA_TARGET_DIR}/bin/conda init bash"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* 1.4 - Disable automatic activation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "${CONDA_TARGET_DIR}/bin/conda config --set auto_activate_base false"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "## 2. Create conda environment"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Create new conda environment. The following steps can be repeated if multiple environments should be created. If the Python version differ towards the external Python version, a mix of Conda modules and external modules will not be possible"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "${CONDA_TARGET_DIR}/bin/conda create -n ${CONDA_ENV} -y python=3.6.8 ipykernel"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "## 3. Create/Edit launch script for the Jupyter kernel"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* 3.1 - Create kernel to allow access to the conda environment. Adapte `module purge` and `PYTHONPATH` according to the comments."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "echo '#!/bin/bash\n",
+ "\n",
+ "# module purge # optional to disable the external environment, necessary, if python version is different\n",
+ " \n",
+ "# Activate your Python virtual environment\n",
+ "source '\"${CONDA_TARGET_DIR}\"'/bin/activate '\"${CONDA_ENV}\"'\n",
+ " \n",
+ "# Ensure python packages installed in conda are always prefered, not necessary if module purge is used\n",
+ "export PYTHONPATH=${CONDA_PREFIX}/lib/python3.6/site-packages:${PYTHONPATH}\n",
+ " \n",
+ "exec python -m ipykernel $@' > ${CONDA_TARGET_DIR}/envs/${CONDA_ENV}/kernel.sh"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "chmod +x ${CONDA_TARGET_DIR}/envs/${CONDA_ENV}/kernel.sh"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "## 4. Create/Edit Jupyter kernel configuration"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* 4.1 - Create user kernel, if you want to access the conda environment of a colleague, only these steps are necessary"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mkdir -p $HOME/.local/share/jupyter/kernels/conda_${CONDA_ENV}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* 4.2 - Adjust kernel.json file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "echo '{\n",
+ " \"argv\": [\n",
+ " \"'\"${CONDA_TARGET_DIR}\"'/envs/'\"${CONDA_ENV}\"'/kernel.sh\",\n",
+ " \"-f\",\n",
+ " \"{connection_file}\"\n",
+ " ],\n",
+ " \"display_name\": \"conda_'\"${CONDA_ENV}\"'\",\n",
+ " \"language\": \"python\"\n",
+ "}' > $HOME/.local/share/jupyter/kernels/conda_${CONDA_ENV}/kernel.json"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Restart of JupyterLab might be necessary to see the kernel in the kernel selection overview."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Bash",
+ "language": "bash",
+ "name": "bash"
+ },
+ "language_info": {
+ "codemirror_mode": "shell",
+ "file_extension": ".sh",
+ "mimetype": "text/x-sh",
+ "name": "bash"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/03-HowTos/Create_JupyterKernel_general.ipynb b/03-HowTos/Create_JupyterKernel_general.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3bca4031121dd244e580170fbe598a67a8dc6703
--- /dev/null
+++ b/03-HowTos/Create_JupyterKernel_general.ipynb
@@ -0,0 +1,469 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "9f53dcb1-00d6-4245-955a-b527f1540865.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "\n",
+ "<h5 style=\"text-align: right\">Author: <a href=\"mailto:j.goebbert@fz-juelich.de?subject=Jupyter-JSC%20documentation\">Jens Henrik Göbbert</a></h5> \n",
+ "<h5><a href=\"../index.ipynb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">Create your own Jupyter Kernel</h1> "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "Often the standard kernel do not provide all features you need for your work. This might be that certain modules are not loaded or packages are not installed. \n",
+ "With your own kernel you can overcome that problem easily and define your own environment, in which you work.\n",
+ "\n",
+ "This notebook shows you how you can build your own kernel for a **python environment**.\n",
+ "\n",
+ "-------------------------"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Building your own Jupyter kernel is a three step process\n",
+ "1. Create/Pimp new virtual Python environment\n",
+ " * venv\n",
+ "2. Create/Edit launch script for the Jupyter kernel\n",
+ " * kernel.sh\n",
+ "3. Create/Edit Jupyter kernel configuration\n",
+ " * kernel.json"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Settings"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* Set kernel name\n",
+ " - must be lower case\n",
+ " - change if you like"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# INPUT NEEDED:\n",
+ "KERNEL_NAME=${USER}_kernel\n",
+ "\n",
+ "export KERNEL_NAME=$(echo \"${KERNEL_NAME}\" | awk '{print tolower($0)}')\n",
+ "echo ${KERNEL_NAME} # double check"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* List directories where JupyterLab will search for kernels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# JUPYTER SEARCH PATH (for kernels-directory)\n",
+ "echo \"jupyter search paths for kernels-directories\"\n",
+ "if [ -z $JUPYTER_PATH ]; then\n",
+ " echo \"$HOME/.local/share/jupyter\"\n",
+ "else\n",
+ " tr ':' '\\n' <<< \"$JUPYTER_PATH\"\n",
+ "fi"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div class=\"alert alert-block alert-info\">\n",
+ "<b>Attention:</b>\n",
+ "Please choose 'private kernel' if you are unsure.</br>\n",
+ "Using 'project kernel's need to be enabled for your project first by our Jupyter-JSC admins.\n",
+ "</div>\n",
+ "\n",
+ "* Set kernel type\n",
+ " - private kernel = \"\\${HOME}/.local/\" \n",
+ " - project kernel = \"\\${PROJECT}/.local/\" \n",
+ " - other kernel = \"\\<your-path\\>\" (ensure it is part of $JUPYTER_PATH or your kernel will not be found by JuypterLab)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# INPUT NEEDED:\n",
+ "export KERNEL_TYPE=private # private, project or other\n",
+ "export KERNEL_SPECS_PREFIX=${HOME}/.local\n",
+ "\n",
+ "###################\n",
+ "# project kernel\n",
+ "if [ \"${KERNEL_TYPE}\" == \"project\" ]; then\n",
+ " export KERNEL_SPECS_PREFIX=${PROJECT}/.local\n",
+ " echo \"project kernel\"\n",
+ "# private kernel\n",
+ "elif [ \"${KERNEL_TYPE}\" == \"private\" ]; then\n",
+ " export KERNEL_SPECS_PREFIX=${HOME}/.local\n",
+ " echo \"private kernel\"\n",
+ "else\n",
+ " if [ ! -d \"$KERNEL_SPECS_PREFIX\" ]; then\n",
+ " echo \"ERROR: please create directory $KERNEL_SPECS_PREFIX\"\n",
+ " fi\n",
+ " echo \"other kernel\"\n",
+ "fi\n",
+ "export KERNEL_SPECS_DIR=${KERNEL_SPECS_PREFIX}/share/jupyter/kernels\n",
+ "\n",
+ "# check if kernel name is unique\n",
+ "if [ -d \"${KERNEL_SPECS_DIR}/${KERNEL_NAME}\" ]; then\n",
+ " echo \"ERROR: Kernel already exists in ${KERNEL_SPECS_DIR}/${KERNEL_NAME}\"\n",
+ " echo \" Rename kernel name or remove directory.\"\n",
+ "fi\n",
+ "\n",
+ "echo ${KERNEL_SPECS_DIR}/${KERNEL_NAME} # double check"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* Set directory for kernels virtual environment\n",
+ " - change if you like"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# INPUT NEEDED:\n",
+ "export KERNEL_VENVS_DIR=${PROJECT}/${USER}/jupyter/kernels\n",
+ "\n",
+ "###################\n",
+ "mkdir -p ${KERNEL_VENVS_DIR}\n",
+ "if [ \"${KERNEL_TYPE}\" != \"private\" ] && [ \"${KERNEL_TYPE}\" != \"other\" ]; then\n",
+ " echo \"Please check the permissions and ensure your project partners have read/execute permissions:\"\n",
+ " namei -l ${KERNEL_VENVS_DIR}\n",
+ "fi\n",
+ "\n",
+ "echo ${KERNEL_VENVS_DIR} # double check\n",
+ "ls -lt ${KERNEL_VENVS_DIR}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Create/Pimp new virtual Python environment"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* 1.1 - Load basic Python module"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "module -q purge\n",
+ "module -q use $OTHERSTAGES \n",
+ "module -q load Stages/2020 2> /dev/null # any stage can be used\n",
+ "module -q load GCCcore/.9.3.0 2> /dev/null\n",
+ "module -q load Python/3.8.5 # only Python is required\n",
+ "module list # double check"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* 1.2 - Load extra modules you need for your kernel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# module load <module you need>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* 1.3 - Create and activate a virtual environment for the kernel \n",
+ "and ensure python packages installed in the virtual environment are always prefered"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if [ -d \"${KERNEL_VENVS_DIR}/${KERNEL_NAME}\" ]; then\n",
+ " echo \"ERROR: Directory for virtual environment already ${KERNEL_VENVS_DIR}/${KERNEL_NAME}\"\n",
+ " echo \" Rename kernel name or remove directory.\"\n",
+ "else\n",
+ " python -m venv --system-site-packages ${KERNEL_VENVS_DIR}/${KERNEL_NAME}\n",
+ " source ${KERNEL_VENVS_DIR}/${KERNEL_NAME}/bin/activate\n",
+ " export PYTHONPATH=${VIRTUAL_ENV}/lib/python3.8/site-packages:${PYTHONPATH}\n",
+ " echo ${VIRTUAL_ENV} # double check\n",
+ "fi"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* 1.4 - Install Python libraries required for communication with Jupyter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "which pip\n",
+ "if [ -z \"${VIRTUAL_ENV}\" ]; then\n",
+ " echo \"ERROR: Virtual environment not successfully initialized.\"\n",
+ "else\n",
+ " pip install --ignore-installed ipykernel\n",
+ " ls ${VIRTUAL_ENV}/lib/python3.8/site-packages/ # double check\n",
+ "fi"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* 1.5 - Install whatever else you need in your Python virtual environment (using pip)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#pip install <python-package you need>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. Create/Edit launch script for the Jupyter kernel"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* 2.1 - Create launch script, which loads your Python virtual environment and starts the ipykernel process inside:\n",
+ "\n",
+ "<div class=\"alert alert-block alert-info\">\n",
+ "<b>Attention:</b>\n",
+ "You MUST load the exactly the same modules as you did above for your virtual Python environment.\n",
+ "</div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "echo '#!/bin/bash'\"\n",
+ "\n",
+ "# Load basic Python module\n",
+ "module purge\n",
+ "module use \"'$OTHERSTAGES'\"\n",
+ "module load Stages/2020\n",
+ "module load GCCcore/.9.3.0\n",
+ "module load Python/3.8.5\n",
+ "\n",
+ "# Load extra modules you need for your kernel (as you did in step 1.2)\n",
+ "#module load <module you need>\n",
+ " \n",
+ "# Activate your Python virtual environment\n",
+ "source ${KERNEL_VENVS_DIR}/${KERNEL_NAME}/bin/activate\n",
+ " \n",
+ "# Ensure python packages installed in the virtual environment are always prefered\n",
+ "export PYTHONPATH=${VIRTUAL_ENV}/lib/python3.8/site-packages:\"'${PYTHONPATH}'\"\n",
+ " \n",
+ "exec python -m ipykernel \"'$@' > ${VIRTUAL_ENV}/kernel.sh\n",
+ "chmod +x ${VIRTUAL_ENV}/kernel.sh\n",
+ "\n",
+ "cat ${VIRTUAL_ENV}/kernel.sh # double check"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3. Create/Edit Jupyter kernel configuration"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* 3.1 - Create Jupyter kernel configuration directory and files"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "python -m ipykernel install --name=${KERNEL_NAME} --prefix ${VIRTUAL_ENV}\n",
+ "export VIRTUAL_ENV_KERNELS=${VIRTUAL_ENV}/share/jupyter/kernels"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* 3.2 - Adjust kernel.json file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mv ${VIRTUAL_ENV_KERNELS}/${KERNEL_NAME}/kernel.json ${VIRTUAL_ENV_KERNELS}/${KERNEL_NAME}/kernel.json.orig\n",
+ "\n",
+ "echo '{\n",
+ " \"argv\": [\n",
+ " \"'${KERNEL_VENVS_DIR}/${KERNEL_NAME}/kernel.sh'\",\n",
+ " \"-m\",\n",
+ " \"ipykernel_launcher\",\n",
+ " \"-f\",\n",
+ " \"{connection_file}\"\n",
+ " ],\n",
+ " \"display_name\": \"'${KERNEL_NAME}'\",\n",
+ " \"language\": \"python\"\n",
+ "}' > ${VIRTUAL_ENV_KERNELS}/${KERNEL_NAME}/kernel.json\n",
+ "\n",
+ "cat ${VIRTUAL_ENV_KERNELS}/${KERNEL_NAME}/kernel.json # double check"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* 3.3 - Create link to kernel specs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mkdir -p ${KERNEL_SPECS_DIR}\n",
+ "cd ${KERNEL_SPECS_DIR}\n",
+ "ln -s ${VIRTUAL_ENV_KERNELS}/${KERNEL_NAME} .\n",
+ "\n",
+ "echo -e \"\\n\\nThe new kernel '${KERNEL_NAME}' was added to your kernels in '${KERNEL_SPECS_DIR}/'\\n\"\n",
+ "ls ${KERNEL_SPECS_DIR} # double check"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4. Cleanup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "deactivate"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Bash",
+ "language": "bash",
+ "name": "bash"
+ },
+ "language_info": {
+ "codemirror_mode": "shell",
+ "file_extension": ".sh",
+ "mimetype": "text/x-sh",
+ "name": "bash"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/001-Jupyter/FAQ_Howto-load-additional-software-modules.ipynb b/03-HowTos/Howto-load-additional-software-modules.ipynb
similarity index 68%
rename from 001-Jupyter/FAQ_Howto-load-additional-software-modules.ipynb
rename to 03-HowTos/Howto-load-additional-software-modules.ipynb
index 4e16e9bee36341e9b31fc415f805ed2071971dee..8d8cfbf323032e36de3f5ff479552c8966c6af1c 100644
--- a/001-Jupyter/FAQ_Howto-load-additional-software-modules.ipynb
+++ b/03-HowTos/Howto-load-additional-software-modules.ipynb
@@ -2,18 +2,17 @@
"cells": [
{
"attachments": {
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B/ZLMQqjiSe6m2ds982/82YfT8h5dMUci6biX5WTyJ7vZ0B7meO3QnGqVgoreW80c8fLXG2ofsB43WRDyZTkfO7X9ev8ATXo6t1hN9nTjz8M8s+VPC0/yiHSwCscH2VMElVjCdMgH0TjNJruMawGU6W455VHqnxvF1r2W5cvCoXa4XTMMxlctqH5UJYm1IeRxj5vrEGz+vW4Hb0SYZPyxxFfIbOjQ+LyFv7Vpurg7r/CTsp7OmPaW0o9JXtQ27c/xN/bp2OsQiaQ3fATDs/w351s62nuYtlxSiQzT22FXUOQGrl/tWwEN01r1iCWWbQgy+kpjnyzn31uXvC8bT7pY4ypA7PJk9IeOrlUe92l+8PNMj7P0Z0hc4815Dieea2Hi5d4Lt5o2gIGlcamGPZWzLZroG5a+mjmi4atI14BHsqC6N67q+02tDAQNGjJ99Nbi7dTFfAaN6iJuhg40+kTRgkvDIjzMixxlSAMcmX0lP2uVXr2+02uoYUcR28joWlccyABnSOVTM9zvc2ZmcGRWMcnkAOyOfA+FbPjmvpZhf7P3zEhew/Z4rw+1UXVb9us9ZSIlsNusyYxioIbi9O4NnId5Nga5AR78VsKPr0sJuLFpZXjC5ZsL5edbIknumnF/bPI6FQFUjGNPt6Nr+vX+mvQLS6xJcCUq9r/ML6/D35raAO0ra3j63xgkA1Hsr36v0ram0YgTondJ0Xm0ekcfvXn7a2B+O2+FObIRNKLLOmXPHtmtkyQ7Qit95DO5lnQYyWUkYyMcfhSMJkuOH8SP0T840m6NrOf5kHDP3jlXyO0kK/8Aci4/Gv3jaQ0+EUX9zQkhg3k4/nTdpvy8P2BkZxWl1Dr4MM0DjiK3mhd5y1Y40X0jWebY40OyOHLhyrXoXX9bHGvRHPPLvrWZ5dzv+s7ns415zzxnn51vNC7zlqxxrUsaK3iFptKKNXFsDnQwo4cBR7C8Tk8KGpQ2OIyKGFHDgOFDCgY5cOXReylgRPIHA8OyB+nRvNC7zlqxxrU0SM3iVrlQGOArPfWGjVh5itKKFHgPmv/EACgQAQABAwIFBQEBAQEAAAAAAAERACExQVEQYXGBkSAwobHw0cFA4f/aAAgBAQABPyGhC9ZA3WmcFD4Xr4FIPypSasZaWLgR2exhlvEq8Ad0q1c8CG0QwzNEkkMSSSNvdKSLiEJvW6/4lWHZgC6tbACpZ+Q068I3LLzT/JI70IgAQBpwtXJGWvL3A7ZoZOOb3HYEwU1CZMAsaVew2wejdmi7It6ZqWahwqUFIJZEFFAhsCq50voLIsgM268AkTogEGvWsk8eAEw1YByYXRNbozSkkxDB1LPigfyakKjcpp2ztMTMSUaPATUlbeATofRPWYnrLX3mnI+ANxQ0Cd65/CTKNGZbYV6b7DHkqdKUdIrLEBZNyo4KJYQsfPz7a15amoy9zxZoF2C78w7PB4bhHWMHdgqTQDapLT+peJYJ8VuL9DdX7u+v1Nq+F+uBT93f0T+VrBgi63KJdNSLEF2l+KeL6uUXJYmLjQb0YCtqwST3pSn0ME8tGZNLw9a/E5cZ+hSNpxNJ8m8IKc2GptGtXukZk+RkjoZo6dgVhtQQKtzB2lD5Wcl7KXnSbIGxCWFqG9GS5kUYUs4fL20/smSihHSB88AGj1h/m7TJAIev3FeGhK3X+rgS++QQ8Aw3RFJRKnRqC0s7lf3d9NaiA8r/AMpEoFHe1fHNyE/Vfu7+ifyIGUwUQ4GNyL/soJd4Bi8YLt2kIFCd0xgbNAvmCUXmsAdNP6nL0Tz0MxPQj5EROtCTprZy0J1VAiWVCgYvjYovXMlpzJmGnOa8jYLs3WKINMAvrZaF4vF7UOZlMl3OoMzEa1K9eQSiNLIztepPpqjinY51fo6lMwyNJ1inu61wKN7NJrUxEjhfMT4qdyr0aLbyrU4OoOQL89KmnrLsfNvDh0nirw7uz4eKjw2EptpXjlwQix6AGVpZ1KG6WvLPfhJRCHmpfRwQwrV5I7kvbnUOQnELDRMVMbqlm4g82Ve1TV4LzJ1DNtZpm8wCWAFNOY0kwLD0oyBLnLRktOricwUwHqPEGXkKxh1IJL2WoUaYBHkFMetNncJSR7p2T/wbNJnJSQML/rFbr4ZIgO8GvWpwHmyDMxERRRBKr6oeEw6Y1TjPahF5PGcl0oHQvFM5wZBRwsM72lNJegobUK63hpzWs3irrcLeazQ49ACTZ+pJqz4QvBkkW7KNJY7qbICCdXa0Jp2Si6TAyEzC7FSLiqmBz5rykVeBDJQnHRigMguwIIIkCM4zin2RTyQK9Jp6m+uyEQp9chrjC7Eu4mlTkncspNmwdn0m/eTgG61IqWPy+516ZrLBd2KkvBHkI7AHnhDxhSHaWrvwYOQ0k4wVibASXjPrgKESzJ657I952P2Lprl9qULKQ1KkWHS+IezNLQYMMEzBC80+3QF5ndGF96dcYTARKQdyn9S5Fgxhv7ZgE5M5651DFIjQwzwFAZbZ+QZ7zwBABVsBrTImQGWgfXc8bgktL2BQqiYgwuGQrfNlJEnmSgAwS6UFYw3jdNBJwrhU5Y9CFW4LAk0WNSrCY8tKRJmb73mvjMgYkFuusBLOdzSYQXmr+dEEUmvXs9KDczv0xoAicl3Kwm8aBABLN76ZKudCakFoMvlTbIECRi2ZWZxUsu62Utw2Zh3ogANg2NgLjY8U34LQSFuouqutwuR0CSym7xntFuZRJbRJzmOVeaufY8uehE0iEJyZXZqjDkjWrBOhZ5K0Gl2xAhIqMjB4p55gm02lUk+gxyllIXXn7jtuFUm3yKCtoiHwpEvcB5/wrQ1RIPp2HoRQJSSYa1E5AFTwEEDFyuUjDt2mgSYwAkdaDEGcw+CgGJjLDLvVmMOi7utJ5MVHcGlyj6UCyCJRyNpp006QfNNNF2Anu70qwRsGDYqPsCXd6yL9iYd6scRsYGxWOliGDlwOguC4I7wbMDCs2mmaZpFoKYBN22axhoEYrSiyJi9ABRUDYo0B4GD2v//aAAwDAQACAAMAAAAQupSd8/0X33x43T9Ry388888xu4pxcpAarw/cCWZjX1w34wffHrtqRCtoBlUCXonEwTiCM7Ohm4qmHTgPF5F7Ok+88888/8QAJxEBAAIBAgQGAwEAAAAAAAAAAQARMSFBUaHR8BBhcZHB4SCBsfH/2gAIAQMBAT8QyUXBE6hbggQFuJh4afAanVxLUVBqb3tBUkuVYhhLGDodSVmjKi1XiPBS4jT1Phigtg6CebBHH5HXcVpGdmn9QENpQFgy2ickzA8z5nNv8nIRIpwP5GaM3b9whqg6a5/c3xXG5zvwwKhSmvt3UqlYd9IDQ99s0ruGEzPrNSr8B6BlIKIoJoZ9YnCwnxQAWwovaWT25reJXzv6xaio22h3UDahMkXKh6QUGo534YTVtd99II3V9wKzH1+/ODiaa17Mq7ygFOsMpeelRrdqv66eDIKnjNTUeVHLSK7E7wPgi1uz/OnOBN8Xn1gAOzTpzggC0PcXBBeEGAMK5fuAbe2nR94VQ2fhjqzzPeOnO9zCr4Tsen5f/8QAJxEBAAIABAUEAwEAAAAAAAAAAQARITFBYVFxocHwEJGx0SCB4fH/2gAIAQIBAT8Qr5gGbENiuMuAVcDRzLPRaDAzlKq1w4VrFCMGS44Ckg1GDlLhalxTLOHBYZwjyO5EVEEtm3ETP8luLbmD3H4Tej4huw1TbOsO06D6nTk6uETePeAxZVR/IkjZMdppluHOdJ3IhaLjZ8Hxl2vN59xG08P8mLVRHMm3BMQ9F7YloLYAli5coaa4HZ0VThzlEUGV6QKGWnKHtm3WLVQlAkZMHK0VFCLjpO5GLovzz7iZer+S6nPy5bbRc5w+T3ljTLz4l1wYRUNeXcL1S6/v36ECLOEw1Zvb1xhut6n0BBqx7/fSKlcFafUUV8Y7b9IpVpGnDRXmEXdEuNw5r4a/rSLCdm+257RAB4dyGKeftKjYaVCjdcesAZfl/8QAJRABAQACAgIBBAMBAQAAAAAAAREAITFBUWEQcYGRoSAw8LHx/9oACAEBAAE/EMavkMFyhAPbj1rMqeXH7QPTlGMU1Z4Wr9ZMI/YTT9ip8r/R5rtHABSqAqG94zyVeSCbNEGzdswKnlTwSkRL7o6/seMIYSREBTytfM6+b/fAybBih0ALnkqQq4D2vK6E1s5MAxCBeaP1+4GDfEFAGgDFguB+OfzKfhId2HomESIjwnyUwBSpVIVk3iLY1MWi2InfnC3JHhgACQBpEZGlxg5niIVZsAN6hJlCA/jCyAQN6LDIk9hFDaADbmoJbSgLREP7+OZlFmAWVXp4yUSRgwIKUs1ipAlkhBoCI02jciZ9xGZ9Eh5hfWChq+3wif7rNSg+7D7ADzN5cWpTbAuIUIMoKIiKNzr5BjdP4ABNeQw984Ug46wMApqLulqDhddqhorWorT0mTBbFFGDZg5VIOZKMs6DKGg1aI2tgKmiBNYOaNvo9/1qzlTG7vSV5BO8P3jkOYnTY4b6r4IFAlEsflge3PKVdEL+VzT8FJwp+v61fynZQv8AI8PjR/tef8NVjd4O4FGP1QxH3SIgF2JFnUEuairmSAe4IjSXbmnGBg0jiBGtr3g1GPEXU0HtxAFGBv2z/m+M9fAMsmhDNEavq4e+8UlkM84EAdkNNCZ0ZBu8ACjokbzupZNDtizW9ZPqb7lsKQIoEjHDDtB7dpJLoDK4dj44fgTipQ6bX+vSGl8ChtXEdC6wRBoj2YTxDKp2+AbTQCuEaGiU/aT7/CjhOXRtH1t+/AmCNUBdDP8An9Pissb9oAQ8BXWGkHvQiR/CfEUD6SdgC/lfjAJg1wEImOHmhyv/AAK/Gf7Xnjyq/FiNxfkaxEZeYkdPp+zCQCXCQABRAAFVy22LhFdbr0XjHFNQgNBtNdYiGER4C+M/3fGevgGsMgOlhcNNiEMOAVMCu+gntQZB51gdck8FqpsURwo0eGydHAY+mMgGutTCu+C67xLmhroIaCNqqBRMaTdHYAooLWZEaq8aXtgdslGxQFEcR/MGioHDNrRTzhhKoz7oUKi4OVAuVqhFiTl0hE5w1GeScNPDQmlGaorhoFK3diprznkT5XToDmPeM8BvFY7mgMCkdFGmJTE6TVZNVFK+QXFX3Yyg6PRD18FhIMKqHgAVcQ6B55pHh2Z5fw2g6Pph+Pz+/gvyUWkX2UJ2mmyPFd6MQLTQOBQiuEUhSW4QyxBxpyOGYcIFDY6XQuDDYCUZOK82QY0UEN6whTmohFOgY5IMhtewAJDsCwpvHH6DVodg1qxCNi2CsDog2+u8uamYCitGbjgWogApENmnY4nCldLRDoHZEQdOjkKMnUXUHegJZoxAL6NhKdNXkqNTCowhj9/YDbJirgYHK1i/wxWyCAt1M6+AfDFVON6ogbCK0NsDa7kxk9FtRehqjw6Y6ZARxwCfbFj7G6sGUdoj3twu1yAonEkNGzDUZzxbbSXbqAD5gpeAJxYr42ksHtj2mZCHBEABROEzo1yOV2v/AKnw29jA6FQLsukHhxtLRhJdABGlRXEsdLWGJxwV1s84I7CFiQNoIobBHFn9oYS/l3iF44cvERXahBYzS7hx/BScxphVGgDtx9VTUJfqbn4JywYCQKMAqvgO3KADdqIJ9ZHn4C2BEhWFBYLPTlLV5otWlKUuaMY40ur57yxs9KqUCjER9iYx7/eQ8v5wAeX84g9/vCec094cCB6w0gnY3IXn95TzgB3+8ePgG8ZOBNuFlHEPr7DIEAE1hDIMbnVMaZqhiQ8mr2MAyTqaKyIIgN9YSNXOI0xQhKDjAAv3fFSEF6WY8xwrtFpBseTJMmTIf5yZMmT6/n5QKpYP34ZzsjZN41rkGT5235xFT6d6pRudL+DrnAKp4A7fWHoRoTmhxOU3ZCBgQ+JHguNRNhJZO5grRr5dqBHFuMEnUkFkJQUNIBsSnDh569oGtCEBxTGj40ASqVaTeCJsSkCwkoJUPRDE+vZmaCKQowgGu8H0ZGEcQ2sdREcjkzaJEFYaG9KcUgfcm3UuLaGiTeXrMKCVDNWqwOCQxIoHk4Rko5iwlrlnCxQmoFD5MRqCU/RBLRtXRBMdRglJ0QsBFOFlCqHn9SbJhGpq4sGc08ghQBBW6zr4B8fuY26mSdQNUQ1HEXEE8E5tFSeBtJ3nHBqs0OwOxK2Mb7StDjrHILUR1hRK62e8NDwOe8AFlsDduEs/A5lGEOqnF/rS4xOG4Psil2sF84ypnU49rD9gxIKOXX+IYcF2lfmg+oT7wJ83wSwWEp4Yu/eI0REwpsYiaxRXYsQZQehh+DFAnMnB9LZ6uAL4UC4GKh4XCljCBTRdNLXjy4E2IFAILFUFPpkgCIDg5dfy5xxHhIpI05aPTww/Jwh5qbPVwsxIut50LvEQJS4aYm3tx4HVE4+jRo0eMNSEoavha26N85xGnQNwKafZjCTVALS6NGjR4zkuU4pyQaNGjHjHn0aFbTlVuuk+Fw2ha36Gz1cfMAq7ONpcF3CwBXFfOeIaySOIdTPxNLS2XxgGdK6WqCcrzleFRyu2Br+r/9k="
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"
}
},
"cell_type": "markdown",
- "metadata": {
- "toc-hr-collapsed": false
- },
+ "metadata": {},
"source": [
- "\n",
- "Author: [Jens Henrik Göbbert](mailto:j.goebbert@fz-juelich.de)\n",
- "------------------------------------"
+ "\n",
+ "<h5 style=\"text-align: right\">Author: <a href=\"mailto:j.goebbert@fz-juelich.de?subject=Jupyter-JSC%20documentation\">Jens Henrik Göbbert</a></h5> \n",
+ "<h5><a href=\"../index.ipynb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">Create your own Jupyter Kernel</h1> "
]
},
{
@@ -22,7 +21,7 @@
"toc-hr-collapsed": false
},
"source": [
- "# How to load additional software modules?\n",
+ "## How to load additional software modules?\n",
"\n",
"[lmod](https://lmod.readthedocs.io) provides on our HPC systems a convenient way to dynamically change the users’ environment. This includes easily adding or removing directories listed in environment variables like PATH or LD_LIBRARY_PATH. \n",
"It is a common approach on high-performance clusters to manage/install/load software packages in multiple versions/optimizations/architectures on the same machine. \n",
diff --git a/001-Jupyter/Markdown_Tipps-and-Tricks.ipynb b/03-HowTos/Markdown_Tipps-and-Tricks.ipynb
similarity index 99%
rename from 001-Jupyter/Markdown_Tipps-and-Tricks.ipynb
rename to 03-HowTos/Markdown_Tipps-and-Tricks.ipynb
index 46cbea75b78c3c404434c779d63eef356bbd67e5..a75360ead644408c0a95684b17ad2df0813a1256 100644
--- a/001-Jupyter/Markdown_Tipps-and-Tricks.ipynb
+++ b/03-HowTos/Markdown_Tipps-and-Tricks.ipynb
@@ -2,8 +2,8 @@
"cells": [
{
"attachments": {
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"
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"
}
},
"cell_type": "markdown",
@@ -11,9 +11,10 @@
"toc-hr-collapsed": false
},
"source": [
- "\n",
- "Author: [Jens Henrik Göbbert](mailto:j.goebbert@fz-juelich.de)\n",
- "------------------------------------"
+ "\n",
+ "<h5 style=\"text-align: right\">Author: <a href=\"mailto:j.goebbert@fz-juelich.de?subject=Jupyter-JSC%20documentation\">Jens Henrik Göbbert</a></h5> \n",
+ "<h5><a href=\"../index.ipynbnb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">Markdown Tipps & Tricks (for Jupyter Notebook)</h1> "
]
},
{
@@ -516,7 +517,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -530,7 +531,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.8"
+ "version": "3.8.5"
}
},
"nbformat": 4,
diff --git a/03-HowTos/Modify_JupyterKernel_at_NotebookRuntime.ipynb b/03-HowTos/Modify_JupyterKernel_at_NotebookRuntime.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8f6ad8cf904e836927a2da5a3b36c9736f414204
--- /dev/null
+++ b/03-HowTos/Modify_JupyterKernel_at_NotebookRuntime.ipynb
@@ -0,0 +1,152 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "03fec242-e656-439b-b99a-8941fcb58603.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "<h5 style=\"text-align: right\">Author: <a href=\"mailto:j.windgassen@fz-juelich.de?subject=Jupyter-JSC%20documentation\">Jonathan Windgassen</a></h5> \n",
+ "<h5><a href=\"../index.ipynb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">How to modify/extend a running Jupyter Kernel</h1> "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are many cases where one needs modules from JupyterLab for a project. But building your own kernel is often a detour from the original idea or is annoying when publishing your project. \n",
+ "By adding these 4 cells to the top of your project you can load modules for the project \"on the fly\".\n",
+ "\n",
+ "Besides that this also adds a ways of installing python packages via pip without disrupting the uses packages or access to the system site-packages\n",
+ "\n",
+ "-------"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- First we create to temp-folders in the */tmp* directory, who will contain the venv where we install the required packages and a folder that stores the PYTHONPATH and LD_LIBRARY_PATH environment variable. This is necessary because loading modules manipulates these variables but we can't access the changes from within python, so we store the changed variables in a folder.\n",
+ "- Then we use a bash-shell to:\n",
+ " - Load the Modules\n",
+ " - Create a venv and installing ipykernel in there\n",
+ " - Write PYTHONPATH and LD_LIBRARY_PATH to the tempdir\n",
+ "- Beacause the Dynamic Linker of Python doesn't detect changes in LD_LIBRARY_PATH we need to reboot the Interpreter afterwards to carry these changes over. To gain access to the venv we will start Python from there.\n",
+ "- After that we install the required modules.\n",
+ "\n",
+ "**Note**: The third cell **won't** show that it's completed and the Notebook will show `Python 3 | Starting` at the bottom, although the interpreter already reloaded compeltely. You can savely ignore this and continue with the third shell. As soon as this has finished the Notebook will show `Python3 | Idle` again."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os, sys, tempfile\n",
+ "\n",
+ "tempdir = tempfile.mkdtemp()\n",
+ "venv_folder = tempfile.mkdtemp()\n",
+ "print(tempdir, venv_folder)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%bash -s \"$tempdir\" \"$venv_folder\" # Pass the paths to the bash-subshell\n",
+ "\n",
+ "######################################################################\n",
+ "## The modules go here ##\n",
+ "## We will use Stage/Devel-2020 with Python 3.8 as a example ##\n",
+ "######################################################################\n",
+ "\n",
+ "# Update to Stage Devel-2020\n",
+ "module --force purge\n",
+ "module use $OTHERSTAGES \n",
+ "module load Stages/Devel-2020\n",
+ "\n",
+ "module load GCC/9.3.0\n",
+ "module load Python/3.8.5\n",
+ "\n",
+ "# Create a venv with the python from Devel-2020 and install ipykernel there (needed for communicating with Jupyter)\n",
+ "# If you don't change Python above this should be a normal Python 3.6 venv\n",
+ "python -m venv --system-site-packages $2\n",
+ "source $2/bin/activate\n",
+ "pip install --quiet ipykernel\n",
+ "\n",
+ "# Store the new variables to the temp-folder\n",
+ "echo $PYTHONPATH > $1/pythonpath\n",
+ "echo $LD_LIBRARY_PATH > $1/librarypath"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The arguments for the new python interpreter. We need to initialize ipykernel or JupyterLab will fail to integrate the new process\n",
+ "args = [f\"{venv_folder}/bin/python\", \"-m\", \"ipykernel\"]\n",
+ "args.extend(sys.argv)\n",
+ "\n",
+ "# Because we call \"execve\" instead of \"execv\" we get the option to set the environment variables in the process. We use this to smuggle in the changed LD_LIBRARY_PATH and PYTHONPATH.\n",
+ "# You also can pass the location of the 2 temp-folders as new environment variables if you want to delete them later for cleanup.\n",
+ "env = {\"PYTHONPATH\": open(f\"{tempdir}/pythonpath\").read(),\n",
+ " \"LD_LIBRARY_PATH\": open(f\"{tempdir}/librarypath\").read(),\n",
+ " \"tempdir\": tempdir,\n",
+ " \"venv_folder\": venv_folder}\n",
+ " \n",
+ "!echo Restarting Interpreter from $venv_folder/bin/python. Please execute the next cell\n",
+ "os.execve(f\"{venv_folder}/bin/python\", args, env)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Because we are in the venv now, we can safely install all packages that we need and don't come with the Python3-Kernel. No need to add --user\n",
+ "%pip install --quiet ..."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "-----\n",
+ "\n",
+ "After that you can import all you libraries (remember that the Interpreter restarted and you need to reimport os/sys/tempfile if you need them) and start with the notebook"
+ ]
+ }
+ ],
+ "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": "3.8.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/001-Jupyter/004-Create_JupyterKernel_container/install-singularity-jupyter-kernel.sh b/03-HowTos/details/install-singularity-jupyter-kernel.sh
similarity index 100%
rename from 001-Jupyter/004-Create_JupyterKernel_container/install-singularity-jupyter-kernel.sh
rename to 03-HowTos/details/install-singularity-jupyter-kernel.sh
diff --git a/001-Jupyter/004-Create_JupyterKernel_container/start_jupyter-jsc.sh b/03-HowTos/details/start_jupyter-jsc_singularity.sh
similarity index 100%
rename from 001-Jupyter/004-Create_JupyterKernel_container/start_jupyter-jsc.sh
rename to 03-HowTos/details/start_jupyter-jsc_singularity.sh
diff --git a/03-HowTos/install-singularity-jupyter-kernel.ipynb b/03-HowTos/install-singularity-jupyter-kernel.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5b558de482960eea0c14ce59cbbd1019fb0aac4d
--- /dev/null
+++ b/03-HowTos/install-singularity-jupyter-kernel.ipynb
@@ -0,0 +1,317 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "dee407d8-ed50-42d4-8200-c39761fee461.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "<!--<h5 style=\"text-align: right\">Author: <a href=\"mailto:@fz-juelich.de?subject=Jupyter-JSC%20documentation\"></a></h5>--><h5 style=\"text-align: right\">Author: Katharina Höflich</h5> \n",
+ "<h5><a href=\"../index.ipynb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">Install containerized Jupyter kernel at Jupyter-JSC</h1> "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This Jupyter notebook will walk you through the installation of a containerized Jupyter kernel (for use at Jupyter-JSC, but it should actually work with any Jupyter server on a system where Singularity is installed). Considerable performance improvements (especially with respect to kernel start-up times) over e.g. conda-based Jupyter kernels on distributed filesystems, as are typically installed on HPC systems, might be experienced. In the example below, the `base-notebook` from the [Jupyter docker stacks](https://jupyter-docker-stacks.readthedocs.io/en/latest/) is used as an IPython kernel (already having the required `ipykernel` package installed), the approach presented here might be extended to any other [Jupyter kernel compatible programming language](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels), though.\n",
+ "\n",
+ "Requirements:\n",
+ "\n",
+ "* Python environment with an installed `ipykernel` package in a Docker (or Singularity) container\n",
+ "* `container` group access for the JSC systems as described [here](https://apps.fz-juelich.de/jsc/hps/juwels/container-runtime.html#getting-access) in the docs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check that the Singularity container runtime is available via the JupyterLab environment,"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "singularity version 3.6.4-1.el8\n"
+ ]
+ }
+ ],
+ "source": [
+ "singularity --version"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Specify the filesystem location that stores the Singularity container image,"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "IMAGE_TARGET_DIR=/p/project/cesmtst/hoeflich1/jupyter-base-notebook"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Optional, if you already have a Singularity container image available at the above location: Convert a containerized Python environment (e.g. the Jupyter `base-notebook` that is [available via Dockerhub](https://hub.docker.com/r/jupyter/base-notebook)) into a Singularity container image to be used as an example here,"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mkdir -p ${IMAGE_TARGET_DIR}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that pulling and converting the Dockerhub image will take a bit of time,"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "singularity pull ${IMAGE_TARGET_DIR}/jupyter-base-notebook.sif docker://jupyter/base-notebook &> singularity.log"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO: Converting OCI blobs to SIF format\n",
+ "INFO: Starting build...\n",
+ "Getting image source signatures\n",
+ "Copying blob sha256:da7391352a9bb76b292a568c066aa4c3cbae8d494e6a3c68e3c596d34f7c75f8\n",
+ "Copying blob sha256:14428a6d4bcdba49a64127900a0691fb00a3f329aced25eb77e3b65646638f8d\n",
+ "Copying blob sha256:2c2d948710f21ad82dce71743b1654b45acb5c059cf5c19da491582cef6f2601\n",
+ "Copying blob sha256:e3cbfeece0aec396b6793a798ed1b2aed3ef8f8693cc9b3036df537c1f8e34a1\n",
+ "Copying blob sha256:48bd2a353bd8ed1ad4b841de108ae42bccecc44b3f05c3fcada8a2a6f5fa09cf\n",
+ "Copying blob sha256:235d93b8ccf12e8378784dc15c5bd0cb08ff128d61b856d32026c5a533ac3c89\n",
+ "Copying blob sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1\n",
+ "Copying blob sha256:b6c06056c45bc1da74604fcf368b02794fe4e36dcae881f4c6b4fa32b37a1385\n",
+ "Copying blob sha256:60918bcbe6d44988e4e48db436996106cc7569a4b880488be9cac90ea6883ae0\n",
+ "Copying blob sha256:762f9ebe4ddc05e56e33f7aba2cdd1be62f747ecd9c8f9eadcb379debf3ebe06\n",
+ "Copying blob sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1\n",
+ "Copying blob sha256:1df9d491a0390ecc3f9fac4484c92b2a5f71a79450017f2fca1849f2d6e7f949\n",
+ "Copying blob sha256:be84c8c720e3c53037ac2c5cbc53cf9a2a674503b2c995da1351e5560f60cc12\n",
+ "Copying blob sha256:28807e96859dc8c00c96255dfa51a0822380638a092803e7143473d1870970fb\n",
+ "Copying blob sha256:bcdaf848f29a8bf0efc18a5883dc65a4a7a6b2c6cf4094e5115188ed22165a00\n",
+ "Copying blob sha256:49777cff52f155a9ba35e58102ecec7029dddf52aa4947f2cffbd1af12848e81\n",
+ "Copying blob sha256:7fb3bffa2e730b052c0c7aabd715303cc5830a05b992f2d3d70afeffa0a9ed4f\n",
+ "Copying blob sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1\n",
+ "Copying config sha256:79f074439b14ae0634f2f217e5debc159c4e8c3a9ff2e0119e4dc88f9c7e21a5\n",
+ "Writing manifest to image destination\n",
+ "Storing signatures\n",
+ "2021/01/19 11:59:33 info unpack layer: sha256:da7391352a9bb76b292a568c066aa4c3cbae8d494e6a3c68e3c596d34f7c75f8\n",
+ "2021/01/19 11:59:34 info unpack layer: sha256:14428a6d4bcdba49a64127900a0691fb00a3f329aced25eb77e3b65646638f8d\n",
+ "2021/01/19 11:59:34 info unpack layer: sha256:2c2d948710f21ad82dce71743b1654b45acb5c059cf5c19da491582cef6f2601\n",
+ "2021/01/19 11:59:34 info unpack layer: sha256:e3cbfeece0aec396b6793a798ed1b2aed3ef8f8693cc9b3036df537c1f8e34a1\n",
+ "2021/01/19 11:59:34 info unpack layer: sha256:48bd2a353bd8ed1ad4b841de108ae42bccecc44b3f05c3fcada8a2a6f5fa09cf\n",
+ "2021/01/19 11:59:34 info unpack layer: sha256:235d93b8ccf12e8378784dc15c5bd0cb08ff128d61b856d32026c5a533ac3c89\n",
+ "2021/01/19 11:59:34 info unpack layer: sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1\n",
+ "2021/01/19 11:59:34 info unpack layer: sha256:b6c06056c45bc1da74604fcf368b02794fe4e36dcae881f4c6b4fa32b37a1385\n",
+ "2021/01/19 11:59:34 info unpack layer: sha256:60918bcbe6d44988e4e48db436996106cc7569a4b880488be9cac90ea6883ae0\n",
+ "2021/01/19 11:59:34 info unpack layer: sha256:762f9ebe4ddc05e56e33f7aba2cdd1be62f747ecd9c8f9eadcb379debf3ebe06\n",
+ "2021/01/19 11:59:34 info unpack layer: sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1\n",
+ "2021/01/19 11:59:34 info unpack layer: sha256:1df9d491a0390ecc3f9fac4484c92b2a5f71a79450017f2fca1849f2d6e7f949\n",
+ "2021/01/19 11:59:36 info unpack layer: sha256:be84c8c720e3c53037ac2c5cbc53cf9a2a674503b2c995da1351e5560f60cc12\n",
+ "2021/01/19 11:59:40 info unpack layer: sha256:28807e96859dc8c00c96255dfa51a0822380638a092803e7143473d1870970fb\n",
+ "2021/01/19 11:59:40 info unpack layer: sha256:bcdaf848f29a8bf0efc18a5883dc65a4a7a6b2c6cf4094e5115188ed22165a00\n",
+ "2021/01/19 11:59:40 info unpack layer: sha256:49777cff52f155a9ba35e58102ecec7029dddf52aa4947f2cffbd1af12848e81\n",
+ "2021/01/19 11:59:40 info unpack layer: sha256:7fb3bffa2e730b052c0c7aabd715303cc5830a05b992f2d3d70afeffa0a9ed4f\n",
+ "2021/01/19 11:59:40 info unpack layer: sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1\n",
+ "INFO: Creating SIF file...\n"
+ ]
+ }
+ ],
+ "source": [
+ "cat singularity.log | grep -v warn"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check that the Singularity image is available,"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "total 177M\n",
+ "drwxr-sr-x 2 hoeflich1 cesmtst 4.0K Jan 19 11:59 .\n",
+ "drwxr-sr-x 5 hoeflich1 cesmtst 4.0K Jan 19 11:59 ..\n",
+ "-rwxr-xr-x 1 hoeflich1 cesmtst 183M Jan 19 11:59 jupyter-base-notebook.sif\n"
+ ]
+ }
+ ],
+ "source": [
+ "ls -lah ${IMAGE_TARGET_DIR}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, setup a Jupyter kernel specification with the `install-jupyter-kernel.sh` script from this repository (which basically writes a `kernel.json` file to the home directory location that Jupyter expects for user-specific kernels),"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "KERNEL_DISPLAY_NAME=Singularity-Python # don't use whitespaces here!\n",
+ "SINGULARITY_IMAGE=${IMAGE_TARGET_DIR}/jupyter-base-notebook.sif"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Link to [install-singularity-jupyter-kernel.sh](https://docs.jupyter-jsc.fz-juelich.de/github/kreuzert/jupyter-jsc-notebooks/blob/documentation/03-HowTos/details/install-singularity-jupyter-kernel.sh)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "./install-singularity-jupyter-kernel.sh ${KERNEL_DISPLAY_NAME} ${SINGULARITY_IMAGE}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check that the Jupyter kernel specification was written,"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{\n",
+ " \"argv\": [\n",
+ " \"singularity\",\n",
+ " \"exec\",\n",
+ " \"--cleanenv\",\n",
+ " \"/p/project/cesmtst/hoeflich1/jupyter-base-notebook/jupyter-base-notebook.sif\",\n",
+ " \"python\",\n",
+ " \"-m\",\n",
+ " \"ipykernel\",\n",
+ " \"-f\",\n",
+ " \"{connection_file}\"\n",
+ " ],\n",
+ " \"language\": \"python\",\n",
+ " \"display_name\": \"Singularity-Python\"\n",
+ "}\n"
+ ]
+ }
+ ],
+ "source": [
+ "cat ${HOME}/.local/share/jupyter/kernels/${KERNEL_DISPLAY_NAME}/kernel.json"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And that the above Singularity-Python kernel is visible by the Jupyter server,"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Available kernels:\n",
+ " singularity-python /p/home/jusers/hoeflich1/juwels/.local/share/jupyter/kernels/Singularity-Python\n",
+ " ruby /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-Ruby/2.6.3-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/ruby\n",
+ " ir35 /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-R/3.5.3-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/ir35\n",
+ " pyquantum-1.0 /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-PyQuantum/1.0-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/pyquantum-1.0\n",
+ " pyparaview-5.8 /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-PyParaView/5.8.0-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/pyparaview-5.8\n",
+ " octave /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-Octave/5.1.0-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/octave\n",
+ " julia-1.4 /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-Julia/1.4.2-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/julia-1.4\n",
+ " javascript /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-JavaScript/5.2.0-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/javascript\n",
+ " cling-cpp17 /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-Cling/0.6-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/cling-cpp17\n",
+ " bash /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-Bash/0.7.1-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/bash\n",
+ " python3 /p/software/juwels/stages/Devel-2019a/software/Jupyter/2019a.2.4-gcccoremkl-8.3.0-2019.3.199-Python-3.6.8/share/jupyter/kernels/python3\n"
+ ]
+ }
+ ],
+ "source": [
+ "jupyter kernelspec list"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If so, you should be able to choose and connect to the containerized Python kernel from the drop down menu and/or the kernel launcher tab (a reload of the JupyterLab web page might be necessary)."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Bash",
+ "language": "bash",
+ "name": "bash"
+ },
+ "language_info": {
+ "codemirror_mode": "shell",
+ "file_extension": ".sh",
+ "mimetype": "text/x-sh",
+ "name": "bash"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/03-HowTos/setup-singularity-jupyter-server.ipynb b/03-HowTos/setup-singularity-jupyter-server.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..186af589009aea5efb8e2a98a7ac1a27ba4c69f4
--- /dev/null
+++ b/03-HowTos/setup-singularity-jupyter-server.ipynb
@@ -0,0 +1,186 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "09224a99-4f86-40e9-8761-b299d24050c2.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "<!--<h5 style=\"text-align: right\">Author: <a href=\"mailto:?subject=Jupyter-JSC%20documentation\">Katharina Höflich</a></h5>--><h5 style=\"text-align: right\">Author: Katharina Höflich</h5> \n",
+ "<h5><a href=\"../index.ipynb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">Setup containerized Jupyter server for Jupyter-JSC</h1> "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This Jupyter notebook will explain how to setup a containerized Jupyter server at Jupyter-JSC. It makes use of the expert features described on page 22 (as of November 25th, 2020) of the training material available [here](https://jupyter-jsc.fz-juelich.de/nbviewer/github/FZJ-JSC/jupyter-jsc-notebooks/blob/master/Jupyter-JSC_supercomputing-in-the-browser.pdf). Please note, that setting up a containerized Jupyter server for the JupyterHub at JSC might introduce certain drawbacks to your Jupyter-JSC experience. Specifically, you will be restricted to the software environment that is installed in your container environment only, which might introduce unwanted side-effects to your JupyterLab-based workflows on the JSC HPC systems. For example, usage of the SLURM batch scheduler commands is not possible, because the SLURM libraries are not visible from the container environment per default. Also, you won't be able to use the Lmod software environment modules provided by JSC. Please note, that if these kind of side-effects are not acceptable, you might rather use a containerized Jupyter kernel as [described here](install-singularity-jupyter-kernel.ipynb). You could also setup your own non-containerized JupyterLab server.\n",
+ "\n",
+ "Please note, you can switch back to the default Jupyter-JSC server environment anytime by deleting `$HOME/.jupyter/start_jupyter-jsc.sh` after [login to the JSC systems](https://apps.fz-juelich.de/jsc/hps/juwels/access.html) via SSH.\n",
+ "\n",
+ "Requirements:\n",
+ "\n",
+ "* Jupyter server environment in a Docker (or Singularity) container\n",
+ "* `container` group access for the JSC systems as described [here](https://apps.fz-juelich.de/jsc/hps/juwels/container-runtime.html#getting-access) in the docs\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Specify the filesystem location that stores the Singularity container image,"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "IMAGE_TARGET_DIR=/p/project/cesmtst/hoeflich1/jupyter-base-notebook"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Convert the example Jupyter base-notebook (that is [available via Dockerhub](https://hub.docker.com/r/jupyter/base-notebook)) into a Singularity container image,"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mkdir -p ${IMAGE_TARGET_DIR}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "singularity pull --force ${IMAGE_TARGET_DIR}/jupyter-base-notebook.sif docker://jupyter/base-notebook &> singularity.log"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO: Using cached SIF image\n"
+ ]
+ }
+ ],
+ "source": [
+ "cat singularity.log | grep -v warn"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check that the Singularity image is available,"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "total 177M\n",
+ "drwxr-sr-x 2 hoeflich1 cesmtst 4.0K Jan 19 18:50 .\n",
+ "drwxr-sr-x 5 hoeflich1 cesmtst 4.0K Jan 19 18:05 ..\n",
+ "-rwxr-xr-x 1 hoeflich1 cesmtst 183M Jan 19 18:50 jupyter-base-notebook.sif\n"
+ ]
+ }
+ ],
+ "source": [
+ "ls -lah ${IMAGE_TARGET_DIR}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, manually (!) specify the Singularity image filesystem location in the `start_jupyter-jsc.sh` script and check that the specified path is correct,"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "#!/bin/bash\n",
+ "\n",
+ "# Author: Katharina Höflich\n",
+ "# Repository: https://github.com/FZJ-JSC/jupyter-jsc-notebooks\n",
+ "\n",
+ "SINGULARITY_IMAGE=/p/project/cesmtst/hoeflich1/jupyter-base-notebook/jupyter-base-notebook.sif\n",
+ "JUPYTERJSC_USER_CMD=\"singularity exec ${SINGULARITY_IMAGE} jupyterhub-singleuser --config ${JUPYTER_LOG_DIR}/.config.py\"\n"
+ ]
+ }
+ ],
+ "source": [
+ "cat start_jupyter-jsc.sh"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And copy the `start_jupyter-jsc_singularity.sh` script to the filesystem location expected by Jupyter-JSC (Link to [start_jupyter-jsc_singularity.sh](https://docs.jupyter-jsc.fz-juelich.de/github/kreuzert/jupyter-jsc-notebooks/blob/documentation/03-HowTos/details/start_jupyter-jsc_singularity.sh))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cp start_jupyter-jsc_singulartiy.sh $HOME/.jupyter/start_jupyter-jsc.sh"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, opening a new Jupyter session via the Jupyter-JSC control panel should now load the containerized Jupyter server that was setup here."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Bash",
+ "language": "bash",
+ "name": "bash"
+ },
+ "language_info": {
+ "codemirror_mode": "shell",
+ "file_extension": ".sh",
+ "mimetype": "text/x-sh",
+ "name": "bash"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
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rename to 04-Tutorials/JupyterWebApplications/voila-examples/4_vuetify-bqplot.ipynb
diff --git a/001-Jupyter/003-JupyterWebApplications/voila-examples/5_vuetify-custom.ipynb b/04-Tutorials/JupyterWebApplications/voila-examples/5_vuetify-custom.ipynb
similarity index 100%
rename from 001-Jupyter/003-JupyterWebApplications/voila-examples/5_vuetify-custom.ipynb
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diff --git a/002-Methods/001-Computing/README.md b/04-Tutorials/placeholder
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rename from 002-Methods/001-Computing/README.md
rename to 04-Tutorials/placeholder
diff --git a/Announcement-2021-11_JupyterLab3_at_Jupyter-JSC.ipynb b/05-News&Updates/Announcement-2021-11_JupyterLab3_at_Jupyter-JSC.ipynb
similarity index 97%
rename from Announcement-2021-11_JupyterLab3_at_Jupyter-JSC.ipynb
rename to 05-News&Updates/Announcement-2021-11_JupyterLab3_at_Jupyter-JSC.ipynb
index b4551e2c50067b5c8ad92a68b6d4a169f68d0bcc..7ec19bdaf656cb1edfd9286b43de1bea2a89c049 100644
--- a/Announcement-2021-11_JupyterLab3_at_Jupyter-JSC.ipynb
+++ b/05-News&Updates/Announcement-2021-11_JupyterLab3_at_Jupyter-JSC.ipynb
@@ -2,8 +2,8 @@
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{
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"
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"
}
},
"cell_type": "markdown",
@@ -12,9 +12,10 @@
"toc-hr-collapsed": false
},
"source": [
- "\n",
- "Author: [Jens Henrik Göbbert](mailto:j.goebbert@fz-juelich.de)\n",
- "------------------------------------"
+ "\n",
+ "<h5 style=\"text-align: right\">Author: <a href=\"mailto:j.goebbert@fz-juelich.de?subject=Jupyter-JSC%20documentation\">Jens Henrik Göbbert</a></h5> \n",
+ "<h5><a href=\"../index.ipynb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">JupyterLab 3.2 on Jupyter-JSC</h1> "
]
},
{
@@ -24,8 +25,6 @@
"toc-hr-collapsed": false
},
"source": [
- "# Welcome JupyterLab 3.2 on Jupyter-JSC\n",
- "\n",
"**JupyterLab** is continuously evolving and the current version 3.2 brings a lot of new fantastic functionalities and possibilities. \n",
"All the more we are happy to announce that we can now provide you with JupyterLab 3 as an access point to our systems via Jupyter-JSC on https://jupyter-jsc.fz-juelich.de.\n",
"\n",
diff --git a/05-News&Updates/Announcement-2022-08_A_new_Jupyter-JSC.ipynb b/05-News&Updates/Announcement-2022-08_A_new_Jupyter-JSC.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..fefd134ffbca7c07b2f28b2276034dacdf6dd06c
--- /dev/null
+++ b/05-News&Updates/Announcement-2022-08_A_new_Jupyter-JSC.ipynb
@@ -0,0 +1,70 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "305e82df-ddd6-439d-bc3d-a036f3e1dc79.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "0be93b1b-f7bb-494f-a0df-9db58fad291c",
+ "metadata": {
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "\n",
+ "<h5 style=\"text-align: right\">Author: <a href=\"mailto:a.grosch@fz-juelich.de?subject=Jupyter-JSC%20documentation\">Alice Grosch</a></h5> \n",
+ "<h5><a href=\"../index.ipynb\">Index</a></h5>\n",
+ "<h1 style=\"text-align: center\">A new Jupyter-JSC</h1> "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f5cc8b62",
+ "metadata": {},
+ "source": [
+ "### Jupyter-JSC just got an overhaul! 🥳 \n",
+ "\n",
+ "#### Backend\n",
+ "Jupyter-JSC got a new and improved backend. This means that Jupyter-JSC will run even more stable in the future and be able to support a larger number of concurrent users! \n",
+ "\n",
+ "Improved starting procedures for services also mean that you now get more detailed and meaningful error messages, should the spawn of your JupyterLab fail.\n",
+ "\n",
+ "#### Frontend\n",
+ "Along with the backend changes, Jupyter-JSC's UI also got updated! To check out the updates, head over to the [documentation](../01-Introduction/03-Using-Jupyter-JSC.ipynb)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "df197372",
+ "metadata": {},
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.8.10 64-bit",
+ "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"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Jupyter-JSC_supercomputing-in-the-browser.pdf b/06-Presentations/Jupyter-JSC_supercomputing-in-the-browser.pdf
similarity index 100%
rename from Jupyter-JSC_supercomputing-in-the-browser.pdf
rename to 06-Presentations/Jupyter-JSC_supercomputing-in-the-browser.pdf
diff --git a/FAQ_HDFCloud.ipynb b/FAQ_HDFCloud.ipynb
deleted file mode 100644
index aaaccec32cf86a8ce91da3ade8b56c0fdb784d9f..0000000000000000000000000000000000000000
--- a/FAQ_HDFCloud.ipynb
+++ /dev/null
@@ -1,235 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Welcome to the Jupyter-JSC Virtual Machine JupyterLab"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Where is this JupyterLab running?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " This JupyterLab instance is running on a [virtual machine](https://www.techopedia.com/definition/4805/virtual-machine-vm) on the [HDF-Cloud](https://www.fz-juelich.de/ias/jsc/EN/Expertise/SciCloudServices/HDFCloud/_node.html). It is started as a [Docker Container](https://www.docker.com/resources/what-container)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## How do I stop this JupyterLab?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " You can stop this JupyterLab in the Control Panel of Jupyter-JSC. \n",
- " You can reach the Control Panel [here](https://jupyter-jsc.fz-juelich.de/hub/start) or in the menu File -> Hub Control Panel. \n",
- " The JupyterLabs will be terminated after 30 days, so make sure your data is backed up."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## How can I upload files to JupyterLab?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " You can use git or you upload files with this button in the top left corner: \n",
- " \n",
- " ![Upload files](https://jupyter-jsc.fz-juelich.de/hub/static/images/upload.png \"Upload Files\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## How can I download a file from JupyterLab?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " Just right click on a file and click \"Download\"."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## What kind of Docker Images do we support?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " You can select between [these eight](https://jupyter-docker-stacks.readthedocs.io/en/latest/using/selecting.html#jupyter-base-notebook) Images. \n",
- " We installed additionally a few JupyterLab extensions. \n",
- " If you want to add your own Docker Image to Jupyter-JSC please contact the <a href=\"mailto:ds-support@fz-juelich.de?subject=Jupyter-JSC Support&body=Please describe your problem here. (english or german)\">Jupyter-JSC support</a>."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## How much memory do I have?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " The amount of memory for each JupyterLab is limited to 4GB. If you try to use more, your kernel will die."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## How much disk space do I have?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " You can store 10 GB of data in /home/jovyan/work. \n",
- " <font color='red'>All files in the ~/work directory will be stored persistently.</font> \n",
- " <font color='red'>All files in the ~/work directory will be accessible in all of your HDF-Cloud JupyterLabs.</font> \n",
- " <font color='red'>Any other files or directories will be deleted if you stop this JupyterLab.</font> \n",
- " Under no circumstances are we liable for any lost data. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Collaborative work\n",
- " We offer four different solutions to share your work with your colleagues. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### GIT\n",
- " You can use the git command in a terminal to work on any git repositories. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Projects\n",
- " Projects are the best way, if you just want to share a few files (or notebooks) with your colleague, that is also using Jupyter-JSC. \n",
- " Projects are directories, that are generated on the [virtual machine](https://www.techopedia.com/definition/4805/virtual-machine-vm) where your JupyterLab is running. \n",
- " If you add another user to your project this directory will be mounted into their [Docker Container](https://www.docker.com/resources/what-container) . \n",
- " You can manage your projects with the following command:\n",
- " \n",
- "```\n",
- " $ bash /home/jovyan/manage_projects.sh \n",
- "```\n",
- " \n",
- " You can store up to 10 GB of data on all your projects combined. \n",
- " If you want to add users to your project you need their email address. It must be the same email address, that is connected to their [JSC webservice credentials](https://judoor.fz-juelich.de/login) . "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### B2DROP"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " You can easily mount your [B2DROP](https://b2drop.eudat.eu) files into this JupyterLab. \n",
- " Just run the command `mount B2DROP` in a terminal and insert your [application credentials](https://eudat.eu/services/userdoc/b2drop#UserDocumentation-B2DROPUsage-WebDavclient)\\. \n",
- " If you want to store your [application credentials](https://eudat.eu/services/userdoc/b2drop#UserDocumentation-B2DROPUsage-WebDavclient) add the following line to /home/jovyan/work/.davfs2/secrets: \n",
- " https://b2drop.eudat.eu/remote.php/webdav \\<USERNAME> \\<PASSWORD> \n",
- " To unmount it run `umount B2DROP`"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### HPCMOUNT"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " You can mount your files on the HPC system with [sshfs](https://en.wikipedia.org/wiki/SSHFS) . \n",
- " For this we created the script at /home/jovyan/mount_hpc.sh . \n",
- " To mount your HPC files just execute \n",
- "\n",
- "```\n",
- "$ bash /home/jovyan/mount_hpc.sh \n",
- "```\n",
- "\n",
- "### What will the script do?\n",
- " If you run it for the first time, it will create a private/public key pair for you. For your safety, a passphrase is required for this key. \n",
- " Afterward, it will upload the public key to your [JUDAC](https://www.fz-juelich.de/ias/jsc/EN/Expertise/Datamanagement/JUDAC/JUDAC_node.html) ~/.ssh/authorized_keys file. This will be done with [UNICORE](https://www.unicore.eu) and the [credentials](https://judoor.fz-juelich.de/login) you used to log in to Jupyter-JSC. \n",
- " If your key is uploaded (or you have already used this script before) it will run the sshfs command to mount your HPC files. \n",
- " You have to enter your passphrase, that you chose in the first run, to use your generated private key.\n",
- "### How can I unmount it?\n",
- " Just run the command \n",
- " ```$ fusermount -u /home/jovyan/HPCMOUNT``` \n",
- "\n",
- " The folder will be automatically unmounted if you stop this JupyterLab. \n",
- "### Where is my private key stored?\n",
- " The private key will be stored, like your other files, in the /home/jovyan/work directory, on a [virtual machine](https://www.techopedia.com/definition/4805/virtual-machine-vm) on the [HDF-Cloud](https://www.fz-juelich.de/ias/jsc/EN/Expertise/SciCloudServices/HDFCloud/_node.html) . \n",
- " The access to this virtual machine is limited to the administrators of Jupyter-JSC. \n",
- " Since your private key is encrypted with a passphrase, even an administrator is not able to use your private key to connect to any HPC system. \n",
- "### I want to mount another account\n",
- " If you have multiple accounts (connected to your [JSC webservice credentials](https://judoor.fz-juelich.de/login)) you can tell the script which one it should use. \n",
- " ```$ bash /home/jovyan/mount_hpc.sh <SYSTEM> <ACCOUNT>``` \n",
- "\n",
- " \\<SYSTEM> is the HPC system that is used to upload your public key. Please use one of these: JURECA, JUWELS, JURON. Your account must have an active project on the chosen system. \n",
- " \\<ACCOUNT> is your HPC account on this system ( and on [JUDAC](https://www.fz-juelich.de/ias/jsc/EN/Expertise/Datamanagement/JUDAC/JUDAC_node.html)) . \n",
- "### I want to delete the public key from my HPC account\n",
- " Jupyter-JSC does not delete anything from your authorized_keys file. So if you want to remove the public key you have to log in to JUDAC and remove it manually. "
- ]
- }
- ],
- "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": "3.7.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/index.ipynb b/index.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..fdb4e20fe6f3b4d78781ec37564c2003336672a0
--- /dev/null
+++ b/index.ipynb
@@ -0,0 +1,136 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "f66d472c-49c1-47d2-8d28-99749aa5c770.png": {
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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "<h5 style=\"text-align: right\">Author: <a href=\"mailto:t.kreuzer@fz-juelich.de?subject=Jupyter-JSC%20documentation\">Tim Kreuzer</a></h5> \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "<h1><center>Jupyter-JSC documentation</center></h1>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "## Introduction\n",
+ "- [JSC](01-Introduction/01-JSC.ipynb)\n",
+ "- [Jupyter-JSC](01-Introduction/02-Jupyter-JSC.ipynb)\n",
+ "- [Using Jupyter-JSC](01-Introduction/03-Using-Jupyter-JSC.ipynb)\n",
+ "- [JupyterLab](01-Introduction/04-JupyterLab.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Configuration\n",
+ "- [Kernel & Proxies](02-Configuration/01-Kernels&Proxies.ipynb) \n",
+ "- [Extensions](02-Configuration/02-Extensions.ipynb)\n",
+ "- [Environment](02-Configuration/03-Environment.ipynb)\n",
+ "- [Two Factor Authentication (2FA)](02-Configuration/2-Factor-Authentication.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Howtos\n",
+ "- Kernel\n",
+ " - [Install kernel with venv](03-HowTos/Create_JupyterKernel_general.ipynb)\n",
+ " - [Install kernel with conda](03-HowTos/Create_JupyterKernel_conda.ipynb)\n",
+ " - [Install singularity kernel](03-HowTos/install-singularity-jupyter-kernel.ipynb)\n",
+ " - [Modify kernel at runtime](03-HowTos/Modify_JupyterKernel_at_NotebookRuntime.ipynb)\n",
+ "- [Run JupyterLab as singularity container](03-HowTos/setup-singularity-jupyter-server.ipynb)\n",
+ "- [JupyterLab markdown tipps & tricks](03-HowTos/Markdown_Tipps-and-Tricks.ipynb)\n",
+ "- [Load additional software modules](03-HowTos/Howto-load-additional-software-modules.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Tutorials\n",
+ "- [Jupyter - general](04-Tutorials/Jupyter-Tutorials/)\n",
+ "- [Jupyter - WebApplications](04-Tutorials/JupyterWebApplications/)\n",
+ "- Books / example material"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [News & Updates](05-News%26Updates/)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Presentations](06-Presentations/)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Community\n",
+ "- [Support / Feedback](mailto:ds-support@fz-juelich.de)\n",
+ "- Feel free to contact us, if you're missing something or want to add documentation."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.8.10 64-bit",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": "shell",
+ "file_extension": ".sh",
+ "mimetype": "text/x-sh",
+ "name": "python",
+ "version": "3.8.10"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}