Delete demo-slides.ipynb

demo-slides.ipynb

@@ -1,260 +0,0 @@
-{
- "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": "64007f68",
-   "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": [
-    "<center>\n",
-    "    <h1> 🚀 Interactive slides from notebook 👋 </h1>\n",
-    "    <br /><br />\n",
-    "    by Piotr Płoński\n",
-    "</center>"
-   ]
-  },
-  {
-   "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": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "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": "cef19a96",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "# Let's generate some data 💻"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "73501174",
-   "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": "0f3058ed",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 1 Axes>"
-      ]
-     },
-     "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": "d10e0704",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 1 Axes>"
-      ]
-     },
-     "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": "code",
-   "execution_count": null,
-   "id": "904aff0d",
-   "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
-}