diff --git a/001-Jupyter/Create_JupyterKernel_conda.ipynb b/001-Jupyter/Create_JupyterKernel_conda.ipynb
index ad40a504980e521ad10de39b06ead61b0791dff9..f34c40e8ba974807b4b0ba9b4434ed4dec6abb87 100644
--- a/001-Jupyter/Create_JupyterKernel_conda.ipynb
+++ b/001-Jupyter/Create_JupyterKernel_conda.ipynb
@@ -2,8 +2,8 @@
"cells": [
{
"attachments": {
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"
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"
}
},
"cell_type": "markdown",
@@ -11,9 +11,20 @@
"toc-hr-collapsed": false
},
"source": [
- "<img src=attachment:09375636-629b-4ee2-9011-455f6157ab16.png title=\"Juelich Supercomputing Centre\" width=\"360\" align=\"left\" style=\"float:right\"/>\n",
- "<H1>Create your own Jupyter CONDA-Kernel</H1>\n",
- "<HR>"
+ "| [Jupyter-JSC](https://jupyter-jsc.fz-juelich.de) ||[JuDoor](https://judoor.fz-juelich.de)|[Juelich Supercomputing Centre](https://www.fz-juelich.de/jsc)|\n",
+ "| :--- | :--- | :--- | ---: |\n",
+ "|||||\n",
+ "| </br>Author:</br> Sebastian Lührs ||||"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "# Create your own Jupyter CONDA-Kernel</H1>\n",
+ "--------------------------------------"
]
},
{
diff --git a/001-Jupyter/Create_JupyterKernel_general.ipynb b/001-Jupyter/Create_JupyterKernel_general.ipynb
index 0e7be26382679c7769eb68d4db690a3e1c8988db..5285f6682f83b42d7ca51ebdff0ed8f39042a204 100644
--- a/001-Jupyter/Create_JupyterKernel_general.ipynb
+++ b/001-Jupyter/Create_JupyterKernel_general.ipynb
@@ -2,8 +2,8 @@
"cells": [
{
"attachments": {
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- "image/gif": 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"
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"
}
},
"cell_type": "markdown",
@@ -11,9 +11,20 @@
"toc-hr-collapsed": false
},
"source": [
- "<img src=attachment:5ccbc8b4-5907-4292-aa1e-6e349c42db5b.gif title=\"Python Logo\" width=\"360\" align=\"left\" style=\"float:right\"/>\n",
- "<H1>Create your own Jupyter Kernel</H1>\n",
- "<HR>"
+ "| [Jupyter-JSC](https://jupyter-jsc.fz-juelich.de) ||[JuDoor](https://judoor.fz-juelich.de)|[Juelich Supercomputing Centre](https://www.fz-juelich.de/jsc)|\n",
+ "| :--- | :--- | :--- | ---: |\n",
+ "|||||\n",
+ "| </br>Author:</br> Jens Henrik Göbbert ||||"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "# Create your own Jupyter Kernel\n",
+ "-------------------------"
]
},
{
diff --git a/001-Jupyter/List_JupyterExtensions.ipynb b/001-Jupyter/List_JupyterExtensions.ipynb
index 9cced3419c40bf39ab204fa188a01a95c36a47f5..580f17b9e007c14fa5c3b47056778c2053f8ff3e 100644
--- a/001-Jupyter/List_JupyterExtensions.ipynb
+++ b/001-Jupyter/List_JupyterExtensions.ipynb
@@ -2,8 +2,8 @@
"cells": [
{
"attachments": {
- "09375636-629b-4ee2-9011-455f6157ab16.png": {
- "image/png": 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"
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"
}
},
"cell_type": "markdown",
@@ -11,9 +11,20 @@
"toc-hr-collapsed": false
},
"source": [
- "<img src=attachment:09375636-629b-4ee2-9011-455f6157ab16.png title=\"Python Logo\" width=\"360\" align=\"left\" style=\"float:right\"/>\n",
- "<H1>List of Extensions on Jupyter-JSC</H1>\n",
- "<HR>"
+ "| [Jupyter-JSC](https://jupyter-jsc.fz-juelich.de) ||[JuDoor](https://judoor.fz-juelich.de)|[Juelich Supercomputing Centre](https://www.fz-juelich.de/jsc)|\n",
+ "| :--- | :--- | :--- | ---: |\n",
+ "|||||\n",
+ "| </br>Author:</br> Jens Henrik Göbbert ||||"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "# List of Extensions on Jupyter-JSC\n",
+ "--------------------------"
]
},
{
diff --git a/001-Jupyter/List_PythonPackages.ipynb b/001-Jupyter/List_PythonPackages.ipynb
index 333fb47517179197ff05d6ace80904598339db45..3391b617e33127830df0849296ca08b2b119fede 100644
--- a/001-Jupyter/List_PythonPackages.ipynb
+++ b/001-Jupyter/List_PythonPackages.ipynb
@@ -2,8 +2,8 @@
"cells": [
{
"attachments": {
- "09375636-629b-4ee2-9011-455f6157ab16.png": {
- "image/png": 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"
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"
}
},
"cell_type": "markdown",
@@ -11,9 +11,20 @@
"toc-hr-collapsed": false
},
"source": [
- "<img src=attachment:09375636-629b-4ee2-9011-455f6157ab16.png title=\"Python Logo\" width=\"360\" align=\"left\" style=\"float:right\"/>\n",
- "<H1>List of included Python packages</H1>\n",
- "<HR>"
+ "| [Jupyter-JSC](https://jupyter-jsc.fz-juelich.de) ||[JuDoor](https://judoor.fz-juelich.de)|[Juelich Supercomputing Centre](https://www.fz-juelich.de/jsc)|\n",
+ "| :--- | :--- | :--- | ---: |\n",
+ "|||||\n",
+ "| </br>Author:</br> Jens Henrik Göbbert ||||"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "toc-hr-collapsed": false
+ },
+ "source": [
+ "# List of included Python packages\n",
+ "-------------------------"
]
},
{
diff --git a/001-Jupyter/Markdown_Tipps-and-Tricks.ipynb b/001-Jupyter/Markdown_Tipps-and-Tricks.ipynb
index 02dfdbcdcadb45680249c91b78a9d94434d4eb09..20a6c4b64ebb485bc49b6b4cc05ed09b489c520e 100644
--- a/001-Jupyter/Markdown_Tipps-and-Tricks.ipynb
+++ b/001-Jupyter/Markdown_Tipps-and-Tricks.ipynb
@@ -2,8 +2,8 @@
"cells": [
{
"attachments": {
- "09375636-629b-4ee2-9011-455f6157ab16.png": {
- "image/png": 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"
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"
}
},
"cell_type": "markdown",
@@ -11,15 +11,18 @@
"toc-hr-collapsed": false
},
"source": [
- "<img src=attachment:09375636-629b-4ee2-9011-455f6157ab16.png title=\"Python Logo\" width=\"360\" align=\"left\" style=\"float:right\"/>\n",
- "<H1>Markdown Tipps & Tricks (for Jupyter Notebook)</H1>\n",
- "<HR>"
+ "| [Jupyter-JSC](https://jupyter-jsc.fz-juelich.de) ||[JuDoor](https://judoor.fz-juelich.de)|[Juelich Supercomputing Centre](https://www.fz-juelich.de/jsc)|\n",
+ "| :--- | :--- | :--- | ---: |\n",
+ "|||||\n",
+ "| </br>Author:</br> Jens Henrik Göbbert ||||"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
+ "# Markdown Tipps & Tricks (for Jupyter Notebook)\n",
+ "------------------\n",
"Markdown writing skills are essential to portray your work in the Jupyter notebook to offer the reader a sufficient explanation of both the code and the concept.\n",
"\n",
"I have collected informations for different sources. Thanks to them!\n",
@@ -30,12 +33,14 @@
"\n",
"## What’s Markdown?\n",
"\n",
- "Markdown is a lightweight Markup language with a plain text syntax. John Gruber developed the Markdown language in 2004 in a collaborative effort with Aaron Swartz, intending to enable people to “write with easy-to-read and easy-to-write plain text format and potentially convert it to structurally correct XHTML (or HTML).” Markdown is designed to be as easy-to-read and easy-to-write as possible. Readability, however, is emphasized above all else.\n",
+ "Markdown is a light markup language with a simple text syntax. Markdown should be easy to write and above all easy to read.\n",
+ "John Gruber developed the Markdown language in 2004 in collaboration with Aaron Swartz with the goal of enabling people to \"write in an easy to read and easy to write plain text format and possibly convert it to structurally correct XHTML (or HTML)\".\n",
+ "However, one should not assume that \"Markdown\" is a substitute for HTML. HTML is a format for publishing, while Markdown is a format for reading. \n",
+ "The syntax of markup is minimal and only applies to a tiny portion of HTML tags. The idea of Markdown is to make it easier to read, write and edit prose, without the intention of creating a syntax that only serves to quickly add HTML tags. Therefore, the formatting syntax of Markdown deals only with questions that can be expressed in plain text.\n",
+ "For everything else, use HTML. You don't have to make any preamble or delimitation to indicate that you are switching from Markdown to HTML - you simply use the tags.\n",
"\n",
- "Nevertheless, Markdown is not a substitute for, or even close to, HTML. Its syntax is minimal, correlating only to a tiny proportion of HTML tags. Markdown’s idea is to make reading, writing, and editing prose easy without the intention to create a syntax that’s just for quickly adding HTML tags. HTML is a format for publishing, while Markdown is a format for reading. Therefore, the formatting syntax of Markdown tackles just issues that can be expressed in plain text. You simply use HTML for any Markup that is not covered by the Markdown syntax. You don’t need to preface it or delimit it to indicate that you are switching from Markdown to HTML — you just use the tags.\n",
- "\n",
- "The following examples start with some examples of easy stuff and then show some tricks not that common. \n",
- "Have fun!"
+ "The following examples start with some simple examples and then show some not so common tricks.\n",
+ "Have fun with them!"
]
},
{
@@ -179,7 +184,7 @@
"* Drop-in image \n",
"You can attach an image (or animated gif) to the notebook and make it part of the .ipynb file. \n",
"Simply drag-and-drop it to the notebook. \n",
- " **ATTENTION** The image will be stored in the specific notebook cell. If you delete the cell it will be gone! "
+ " **ATTENTION** The image will be stored in the notebook. If you do not reference to them any more they will automatically be removed from the notebook! "
]
},
{
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
new file mode 100644
index 0000000000000000000000000000000000000000..031c2153ae037c81c720c241d443e845c721cc25
--- /dev/null
+++ b/002-Methods/004-Dashboards/001-Dash/001-Getting_Started/getting_started.ipynb
@@ -0,0 +1,340 @@
+{
+ "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
new file mode 100644
index 0000000000000000000000000000000000000000..0e178a869d43e7670553bdb25ac78fd00ce58c96
--- /dev/null
+++ b/002-Methods/004-Dashboards/001-Dash/001-Getting_Started/getting_started.py
@@ -0,0 +1,223 @@
+# ---
+# 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")
+# ```
+
+# %%