{
"cells": [
{
"cell_type": "raw",
"id": "833c8e20",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"---\n",
"title: 🚀 Demo notebook\n",
"description: Simple notebook with widgets demo\n",
"output: slides\n",
"show-code: False\n",
"params:\n",
" name:\n",
" input: text\n",
" label: What is your name?\n",
" value: Piotr\n",
" mu: \n",
" input: slider\n",
" label: X-data mean\n",
" value: 0\n",
" min: -5\n",
" max: 5\n",
" sigma:\n",
" input: numeric\n",
" label: X-data sigma\n",
" value: 1\n",
" min: 0\n",
" max: 3\n",
" step: 0.01\n",
" points:\n",
" input: select\n",
" label: How many points?\n",
" value: 100\n",
" choices: [50, 100, 200, 500, 1000]\n",
" ---"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d177ab3a",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"from IPython.display import Markdown as md\n",
"from matplotlib import pyplot as plt\n",
"from random import gauss"
]
},
{
"cell_type": "markdown",
"id": "c418de47",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"\n",
" 🚀 Interactive slides from notebook 👋
\n",
"
\n",
" by Piotr Płoński\n",
""
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "05963627",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"name = \"Piotr\"\n",
"mu = 0\n",
"sigma = 1\n",
"points = 100"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "bf1faf4b",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/markdown": [
"# Welcome Piotr! 😀\n",
"\n",
"This presentation is interactive. You can change parameters on the left sidebar. \n",
"Please click `Run` to recompute the presentation with new values. \n",
"\n",
"How does it work?\n",
"\n",
"- The presentation was created in Jupyter Notebook and is converted to slides with [reveal.js](https://github.com/hakimel/reveal.js/) package.\n",
"- The interactive widgets are constructed by [Mercury](https://github.com/mljar/mercury) based on YAML config\n",
"- The presentation is served in HuggingFace Spaces!\n"
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"md(f\"\"\"# Welcome {name}! 😀\n",
"\n",
"This presentation is interactive. You can change parameters on the left sidebar. \n",
"Please click `Run` to recompute the presentation with new values. \n",
"\n",
"How does it work?\n",
"\n",
"- The presentation was created in Jupyter Notebook and is converted to slides with [reveal.js](https://github.com/hakimel/reveal.js/) package.\n",
"- The interactive widgets are constructed by [Mercury](https://github.com/mljar/mercury) based on YAML config\n",
"- The presentation is served in HuggingFace Spaces!\n",
"\"\"\")"
]
},
{
"cell_type": "markdown",
"id": "c513d59d",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Let's generate some data 💻"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "44ecce51",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"# random data from gaussian distribution\n",
"data_x = [gauss(mu, sigma) for _ in range(int(points))]\n",
"data_y = [gauss(0, 1) for _ in range(int(points))]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1b26db10",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12,6))\n",
"plt.rcParams.update({'font.size': 22})\n",
"_ = plt.hist(data_x, bins=40, label=\"X-data\", alpha=0.5)\n",
"_ = plt.hist(data_y, bins=40, label=\"Y-data\", alpha=0.5)\n",
"_ = plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "200b4030",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12,6))\n",
"_ = plt.plot(data_x, data_y, 'o')\n",
"_ = plt.title(\"Random points scatter plot\")\n",
"_ = plt.hlines(0, min(data_x+[0]), max(data_x+[0]), color=\"lightgray\")\n",
"_ = plt.vlines(0, min(data_y), max(data_y), color=\"lightgray\")"
]
},
{
"cell_type": "markdown",
"id": "66073398",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Thank you!\n",
"\n",
"📚 Read more about Mercury:\n",
"\n",
"- Website https://mljar.com/mercury/\n",
"- GitHub https://github.com/mljar/mercury\n",
"- Email contact@mljar.comm\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "330dc133",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Slideshow",
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}