presentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "115b0e7f",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Presentation from Jupyter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "454cf5d2",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "You can create presentation from Jupyter Notebook.\n",
    "\n",
    "Mixing code and Markdown is just fantastic:\n",
    "- computation presentations are great,\n",
    "- I like to code, I don't like to manually copy-paste images,\n",
    "- sharing presentation as website or PDF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cee93ddd",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "\n",
       "## Hello World!\n",
       "\n",
       "You can mix Markdown with variables thanks to `IPython.display.Markdown()`.\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Markdown\n",
    "\n",
    "greetings = \"Hello\"\n",
    "name = \"World\"\n",
    "Markdown(f\"\"\"\n",
    "\n",
    "## {greetings} {name}!\n",
    "\n",
    "You can mix Markdown with variables thanks to `IPython.display.Markdown()`.\n",
    "\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8574049",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Simple plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a956ad9",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import random\n",
    "\n",
    "points_count = 10\n",
    "color = \"blue\"\n",
    "x = [random.gauss(0, 1) for _ in range(points_count)]\n",
    "y = [random.gauss(0, 1) for _ in range(points_count)]\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "_ = plt.plot(x, y, '.', markersize=14, color=color)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5317e29a",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "notes "
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "presenv",
   "language": "python",
   "name": "presenv"
  },
  "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}