{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<div style=\"text-align: center; margin-top: 18%; margin-bottom: 4%;\">\n",
    "    <h1 style=\"text-align: center; font-size: 2.5em\">\n",
    "      Interactive Computing with Jupyter and Almond</h1>\n",
    "    <div style=\"text-align: center; border: 1px solid black; width: 50%; margin: 1em auto; font-style: italic; font-size: 0.8em\">\n",
    "    <p style=\"text-align: center;\">space/shift+space to navigate slides</p>\n",
    "    <p style=\"text-align: center;\">ctrl+enter to run code</p>\n",
    "    <p style=\"text-align: center;\">up/down to select the previous/next code cell</p>\n",
    "    <p style=\"text-align: center;\">optimized for full-screen mode</p>\n",
    "    </div>\n",
    "</div>\n",
    "\n",
    "<img src=\"img/logo-katana.svg\" class=\"float-left\" style=\"margin-bottom: 0\" width=\"20%\"/>\n",
    "\n",
    "<p class=\"float-right\" style=\"text-align: right\">Sören Brunk<br>\n",
    "<img style=\"display: inline; height: 1em\" src=\"img/Twitter_bird_logo_2012.svg\"  /> @soebrunk</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# How Do We learn?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<img src=\"img/learning-phases.svg\" width=\"80%\" style=\"margin: 20rem auto;\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Exploration is a Feedback Loop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "\n",
    "\n",
    "<div style=\"margin-top:4%\">\n",
    "\n",
    "<img width=\"90%\" src=\"img/exploration_feedback_loop.svg\"/>\n",
    "\n",
    "\n",
    "</div>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<p style=\"margin: 0 auto\">\n",
    "    <img style=\"margin: 0 auto\" src=\"img/fast-feedback.jpg\" width=\"100%\"/>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Interactive Computing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "> An Interactive computation is a persistent computer program that runs with a \"human in the loop,\" where the primary mode of interaction is through the human interactively writing/running blocks of code and looking at the result.\n",
    ">  \n",
    ">  &mdash; Brian Granger (co-founder of Project Jupyter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<h1 align=\"center\">The Scala 2 REPL</h1>\n",
    "\n",
    "<img src=\"img/repl.png\" width=\"75%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<h1 align=\"center\">Ammonite</h1>\n",
    "\n",
    "<img src=\"img/ammonite-screenshot.png\" width=\"75%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Limitations of the REPL Design"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Optimized for **exploration from scratch**\n",
    " * Write code and see outputs fast (feedback loop)\n",
    "* Not ideal for reading, copying or changing code\n",
    "   * Interleaved inputs and outputs\n",
    "   * History cluttered with refinement attempts\n",
    "   * Dependent expressions\n",
    "* Sharing is difficult\n",
    "  * Not persistent except history\n",
    "* **Observe and replicate phases not part the of REPL design**\n",
    "\n",
    "<img  src=\"img/learning-phase-explore.svg\" width=\"50%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<h1 align=\"center\">Learning with the REPL</h1>\n",
    "\n",
    "<img src=\"img/browser-repl-screenshot.png\" width=\"70%\" />\n",
    "\n",
    "<img  src=\"img/learning-phase-explore.svg\" width=\"50%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Worksheets\n",
    "\n",
    "> A worksheet is a Scala file that is evaluated on save, and the result of each expression is shown in a column to the right of your program. Worksheets are like a REPL session on steroids, and enjoy 1st class editor support: completion, hyperlinking, interactive errors-as-you-type, etc.\n",
    ">\n",
    ">  &mdash; <em>Source: <a href=\"https://dotty.epfl.ch/docs/usage/worksheet-mode.html\">Dotty documentation</a></em>\n",
    "\n",
    "<img src=\"img/dotty-worksheet.png\" width=\"75%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Online Worksheets\n",
    "\n",
    "<img src=\"img/scastie1.png\" width=\"90%\" />\n",
    "\n",
    "<img src=\"img/scalafiddle1.png\" width=\"90%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Learning with Worksheets\n",
    "\n",
    "<img src=\"img/browser-worksheet-screenshot.png\" width=\"90%\" />\n",
    "\n",
    "<img style=\"clear: both\" src=\"img/learning-phase-explore-replicate.svg\" width=\"45%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# A More Integrated Experience?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<div style=\"background: #5CC6E4; min-height: 120px; margin:0 -20px;\">\n",
    "<p style=\"text-transform: uppercase;\n",
    "    font-weight: 900;\n",
    "    font-size: 2.875rem;\n",
    "    color: #2c90ad;\n",
    "    margin-bottom:0;\n",
    "    padding-top: 2rem;\n",
    "    padding-left: 2rem\">\n",
    "Tour of Scala</p>\n",
    "<p style=\"font-size: 3.875rem;\n",
    "    font-family: 'Lato', sans-serif;\n",
    "    padding-top: 0rem;\n",
    "    padding-left: 2rem;\n",
    "    text-transform: uppercase;\n",
    "    text-shadow: rgba(0, 43, 54, 0.1) 2px 2px 0;\n",
    "    color: #fff;\n",
    "    font-weight: 900;\n",
    "    margin: 0;\">Higher-Order-Functions\n",
    "</p>\n",
    "</div>\n",
    "\n",
    "<p style=\"font-size: 0.8em\">Higher order functions take other functions as parameters or return other functions ...</p>\n",
    "<p style=\"font-size: 0.8em\">One of the most common examples is the higher-order function <code>map</code> which is available for collections in Scala.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_style": "center",
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "// Use ctrl+enter to run the cell\n",
    "val salaries = Seq(20000, 70000, 40000)\n",
    "val doubleSalary = (x: Int) => x * 2\n",
    "val newSalaries = salaries.map(doubleSalary)\n",
    "salaries.map(x => x *3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<p style=\"font-size: 0.8em\"><code>doubleSalary</code> is a function which takes a single Int, <code>x</code>, and returns <code>x * 2</code>. In general, the tuple on the left of the arrow <code>=></code> is a parameter list and the value of the expression on the right is what gets returned. On line 3, the function <code>doubleSalary</code> gets applied to each element in the\n",
    "list of salaries.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<h1 align=\"center\">How?</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* We're actually in a Jupyter notebook!\n",
    "* It's just shown as a slideshow (using the RISE plugin)\n",
    "* Usually, it looks more like this:\n",
    "\n",
    "<img style=\"border: 1px solid gray;\" src=\"img/jupyter-notebook-screenshot.png\" width=\"80%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<h1 align=\"center\">Notebook Basics</h1>\n",
    "\n",
    "* Jupyter notebooks are **interactive, web based documents**\n",
    "* They are made of **cells**\n",
    "* Two cell types\n",
    " * **Documentation**\n",
    " * **Code**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Documentation Cells\n",
    "\n",
    "* All the **usual** [Markdown](https://daringfireball.net/projects/markdown/) *formatting* `options`\n",
    "* Images ![Scala](../static/base/images/logo.png)\n",
    "* Code fences\n",
    "```scala\n",
    "val x = 23\n",
    "```\n",
    "* LaTeX equations $$e^x=\\sum_{i=0}^\\infty \\frac{1}{i!}x^i$$\n",
    "* We can **edit them right in the browser!**\n",
    "\n",
    "*Double click to enter edit mode, then hit ctrl+enter to return to the rendered view*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Code Cells\n",
    "\n",
    "* Write code\n",
    "* Evaluate code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "case class Person(name: String, age: Int)\n",
    "\n",
    "val alice = Person(\"alice\", 42)\n",
    "val bob = Person(\"bob\", 43)\n",
    "\n",
    "Seq(alice, bob).map(_.name.capitalize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Mix and Match\n",
    "\n",
    "* We can have any number of cells in a notebook\n",
    "* We can freely mix code cells with documentations cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "\"I'm a code cell, evaluate me!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "println(\"I'm so effectful!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "* We can even put cells side-by-side   \n",
    "  (with the help of a plugin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_style": "split",
    "scrolled": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "\"I'm smaller, but you can still evaluate me.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Notebooks Integrate Rich Documentation with Runnable Code\n",
    "\n",
    "<img style=\"width: 45%;\" src=\"img/browser-repl-screenshot-cropped.png\"/>\n",
    "\n",
    "<div style=\"text-align:center; font-size: 1.5em;\">⬇</div>\n",
    "<img src=\"img/jupyter-notebook-screenshot.png\" width=\"45%\" />\n",
    "\n",
    "<img style=\"clear: both\" src=\"img/learning-phases-only.svg\" width=\"45%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Notebooks are Worksheets in a REPL\n",
    "\n",
    "<img src=\"img/repl-worksheets.png\" width=\"60%\" />\n",
    "\n",
    "<div style=\"text-align:center; font-size: 2em;\">⬇</div>\n",
    "<img src=\"img/jupyter-notebook-screenshot.png\" width=\"52%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Notebooks are Worksheets in a REPL\n",
    "\n",
    "# &rarr; [DEMO](../lab/tree/worksheets-repl.ipynb) &larr;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Exploring Data with the REPL\n",
    "\n",
    "<img src=\"img/spark-shell-viz.png\" width=\"70%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Exploring Data with Notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "interp.repositories() ++= Seq(coursier.maven.MavenRepository(\"https://jitpack.io\"))\n",
    "import $ivy.`org.apache.spark::spark-sql:2.4.3` // Or use any other 2.x version here\n",
    "import $ivy.`sh.almond::almond-spark:0.5.0`\n",
    "import org.apache.spark.sql._, org.apache.log4j.{Level, Logger}\n",
    "Logger.getLogger(\"org\").setLevel(Level.OFF)\n",
    "\n",
    "val spark = {\n",
    "  NotebookSparkSession.builder()\n",
    "    .progress(false)\n",
    "    .master(\"local[*]\")\n",
    "    .getOrCreate()\n",
    "}\n",
    "def sc = spark.sparkContext"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Exploring Data with Notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import spark.implicits._\n",
    "import $file.SparkHelpers, SparkHelpers._\n",
    "\n",
    "val titanic = spark.read\n",
    "  .format(\"csv\")\n",
    "  .option(\"inferSchema\", \"true\").option(\"header\", \"true\")\n",
    "  .load(\"titanic.csv\")\n",
    "\n",
    "titanic.showHTML()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Exploring Data with Notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import $ivy.`org.vegas-viz::vegas-spark:0.3.12-SNAPSHOT`\n",
    "import vegas._, vegas.data.External._, vegas.sparkExt._\n",
    "Vegas().\n",
    "  withDataFrame(titanic).\n",
    "  mark(vegas.Bar).\n",
    "  encodeY(\"*\", aggregate=AggOps.Count, axis=vegas.Axis(title=\"Number of People\", grid=false)).\n",
    "  encodeColumn(\"Pclass\", Ord, scale=Scale(padding=16.0), axis=vegas.Axis(orient=Orient.Bottom, axisWidth=1.0, offset= -8.0)).\n",
    "  encodeX(\"Survived\", Nominal, scale=Scale(bandSize = 20.0), hideAxis=true).\n",
    "  encodeColor(\"Survived\", Nominal, scale=Scale(rangeNominals=List(\"red\", \"green\"))).\n",
    "  configFacet(cell=CellConfig(strokeWidth = 0)).configCell(height=400).show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Exploring Data with Notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import $ivy.`org.plotly-scala::plotly-almond:0.7.0`\n",
    "import plotly._, plotly.element._, plotly.layout._, plotly.Almond._\n",
    "\n",
    "val trace1 = plotly.Bar(\n",
    "  Seq(\"giraffes\", \"orangutans\", \"monkeys\"), Seq(20, 14, 23), name = \"SF Zoo\"\n",
    ")\n",
    "val trace2 = plotly.Bar(\n",
    "  Seq(\"giraffes\", \"orangutans\", \"monkeys\"), Seq(15, 18, 29), name = \"LA Zoo\"\n",
    ")\n",
    "val data = Seq(trace1, trace2)\n",
    "val layout = Layout(barmode = BarMode.Group)\n",
    "\n",
    "plot(data, layout)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "But we aren't restricted to visualizing data. We can also visualize code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Visualizing Code with Notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import $ivy.`io.github.stanch::reftree:1.4.0`\n",
    "import reftree.render._, reftree.diagram._, reftree.contrib.SimplifiedInstances.string\n",
    "\n",
    "case class Person(name: String, age: Int)\n",
    "val people = List(Person(\"Alice\", 29), Person(\"Bob\", 25))\n",
    "\n",
    "val renderer = Renderer(renderingOptions = RenderingOptions(density = 80))\n",
    "renderer.render(\"example\", Diagram.sourceCodeCaption(people))\n",
    "Image.fromFile(\"example.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Notebooks Enable Rich Embedded Output\n",
    "\n",
    "<img class=\"float-left\" style=\"margin-top: 3%\" src=\"img/spark-shell-viz.png\" width=\"47%\" />\n",
    "<div class=\"float-left\" style=\"margin: 15% 3rem; font-size: 2em;\">➡</div>\n",
    "<img class=\"float-left\" src=\"img/spark-notebook-viz.png\" width=\"45%\" />"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<h1 align=\"center\">Project Jupyter</h1>\n",
    "\n",
    "<img src=\"attachment:image.png\" width=\"90%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Jupyter History"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "2001: IPython Shell\n",
    "<img src=\"img/ipython-6-screenshot.png\" width=\"80%\"/>\n",
    "\n",
    "2011: IPython Notebook\n",
    "<img src=\"img/ipython-notebook.png\" width=\"80%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "2014: Project Jupyter (support for other languages besides Python)\n",
    " \n",
    "<img style=\"\" src=\"http://localhost:8888/static/base/images/logo.png\"/>\n",
    "\n",
    " <div class=\"flex-row\" style=\"width: 40%; margin: auto auto\">\n",
    "  <div class=\"flex-col-3\">\n",
    "    <img src=\"img/julia-dots.svg\" alt=\"Julia\">\n",
    "  </div>\n",
    "  <div class=\"flex-col-3\">\n",
    "    <img src=\"img/Python-logo-notext.svg\" alt=\"Python\">\n",
    "  </div>\n",
    "  <div class=\"flex-col-3\">\n",
    "    <img src=\"img/R_logo.svg\" alt=\"R\">\n",
    "  </div>\n",
    "</div> \n",
    "\n",
    "2018: JupyterLab (next generation UI)\n",
    "<img src=\"img/jupyterlab.png\" width=\"80%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<h1 align=\"center\">High-Level Jupyter Architecture</h1>\n",
    "\n",
    "<img width=\"70%\" src=\"img/notebook_components.png\" alt=\"Jupyter components\"/>\n",
    "<p style=\"text-align:center;\"><em>(source: <a href=\"https://jupyter.readthedocs.io/en/latest/architecture/how_jupyter_ipython_work.html#id5\">Jupyter documentation</a>)</em></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Jupyter Frontends\n",
    "\n",
    "* Classic Notebook - first Jupyter UI, still widely used\n",
    "* JupyterLab - modern frontend implementation\n",
    "* nteract - minimalistic desktop based frontend\n",
    "* Hydrogen atom plugin - worksheet like with Jupyter backend\n",
    "* IntelliJ and VS Code also have limited notebook support\n",
    " * Currently restricted to Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<h1 align=\"center\">JupyterLab</h1>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "* Modernized UI\n",
    "* Modular, component based, modern web technologies\n",
    "* More than notebooks:\n",
    " * File manager\n",
    " * Editor\n",
    " * Terminal\n",
    " * Plugins\n",
    "* Close to a 1.0 release"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "![JupyterLab](img/jupyterlab.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# The Jupyter Protocol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<img class=\"float-left\" src=\"img/jupyter-protocol-screenshot.png\"/>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<img src=\"img/jupyter-msg.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Kernels\n",
    "\n",
    "\n",
    "<img style=\"width: 60%;\" src=\"img/kernels-jlab-launcher.png\">\n",
    "\n",
    "\n",
    "* Responsible for running code\n",
    "* Don't know anything about notebooks\n",
    "* Can be implemented in any language\n",
    "  * Have to support the Jupyter protocol\n",
    "* ~ 100 different Kernels available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<h1 align=\"center\">The almond Scala Kernel</h1>\n",
    "<img src=\"img/almond-website.png\" width=\"65%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Ammonite Niceties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "* Dynamic dependency resolution via Coursier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import $ivy.`org.typelevel::squants:1.4.0`\n",
    "import squants.energy.Kilowatts, squants.time.Hours\n",
    "\n",
    "val load = Kilowatts(1.2)\n",
    "val time = Hours(2)\n",
    "val energyUsed = load * time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Syntax highlighting and pretty printing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "Seq.fill(3)(Seq.fill(5)(f\"${scala.util.Random.nextFloat}%1.4f\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Not A Full Blown IDE - But\n",
    "\n",
    "* Autocompletion for regular code and ivy imports\n",
    "* Type hints & metabrowse via shift + tab (work in progress)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import $ivy.`org.typelevel::squants:` // put the cursor after the last colon and press shift, select 1.4.0\n",
    "\n",
    "Seq(\"alice\", \"bob\", \"charlie\").map(name => name.) // put the cursor after name. and press shift, select capitalize \n",
    "// Run the cell, then put the cursor on map and press shift+tab multiple times until you get a type hint at the bottom\n",
    "// Click on the link int the type hint for a metabrowse window"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Using the Ammonite API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "repl.history.takeRight(3).toList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "interp.repositories().last"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Rich Outputs with the almond Jupyter API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_style": "split",
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import almond.display._\n",
    "\n",
    "Html(\"Some <b>bold</b> text\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_style": "split",
    "scrolled": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "Svg(\"\"\"<svg height=\"250\" xmlns=\"http://www.w3.org/2000/svg\">\n",
    "        <rect x=\"25\" y=\"25\" width=\"200\" height=\"200\" fill=\"lime\" stroke-width=\"4\" stroke=\"pink\" />\n",
    "        <circle cx=\"125\" cy=\"125\" r=\"75\" fill=\"orange\" />\n",
    "       </svg>\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "Image.from(url = \"http://localhost:8888/static/base/images/logo.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "Latex(\"\"\"$$e^x=\\sum_{i=0}^\\infty \\frac{1}{i!}x^i$$\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "Javascript(\"alert('Hello')\") // JS execution is restricted in newer frontends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Finally some Progress..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "val handle = ProgressBar(10,100)\n",
    "  .withHtmlWidth(\"100%\")\n",
    "  .withLabel(\"Overall Progress\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "for {i <- 1 to 100} {\n",
    "  handle.withProgress(i).update()\n",
    "  Thread.sleep(30)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Reading User Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "val result = Input().withPrompt(\">>> \").request()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Updating Vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import scala.concurrent.Future\n",
    "implicit val ec = scala.concurrent.ExecutionContext.global\n",
    "\n",
    "val f = Future {\n",
    "  Thread.sleep(10000)\n",
    "  \"finished\"\n",
    "}\n",
    "\n",
    "val v = \"a val\"\n",
    "var x = \"a var that we'll update\"\n",
    "var y = \"another var\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "x = \"updated x\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Using the almond APIs for Library Integrations\n",
    "\n",
    "build.sbt of your library:\n",
    "```scala\n",
    "libraryDependencies += (\"sh.almond\" % \"scala-kernel-api\" % \"0.5.0\" % Provided)\n",
    "  .cross(CrossVersion.full))\n",
    "```\n",
    "Your library code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "  def randomEmoji()(implicit kernel: almond.api.JupyterApi): Unit = {\n",
    "  val emoji = Character.toChars(scala.util.Random.nextInt(0x1F64F - 0x1F600) + 0x1F600).mkString\n",
    "  kernel.publish.html(s\"\"\"<p style=\"font-size:4em; text-align: center\">$emoji</p>\"\"\")}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Some notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "// import $ivy.`my:library:1.0`\n",
    "randomEmoji()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Teaching with Notebooks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# How To Make Your Existing Documentation Interactive\n",
    "\n",
    "* Many tutorials are already Markdown with examples in code fences\n",
    "  * Often even checked via **tut** or **mdoc**\n",
    "* Notebook contents are very similar, just encoded differently (JSON)\n",
    "  * Tools like https://github.com/aaren/notedown let us convert markdown with code fences to notebooks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Share Your Runnable Notebooks With Binder\n",
    "\n",
    "<img src=\"img/binder.png\" width=\"55%\" />\n",
    "\n",
    "# &rarr; [DEMO](https://mybinder.org/v2/gh/almond-sh/examples/master?urlpath=lab%2Ftree%2Fnotebooks%2Findex.ipynb) &larr;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Ok this looks cool, where do I find more Info?\n",
    "\n",
    "<h2 style=\"text-align: center;\"><a href=\"https://jupyter.org\">https://jupyter.org</a></h2>\n",
    "\n",
    "<h2 style=\"text-align: center;\"><a href=\"https://almond.sh\">https://almond.sh</a></h2>\n",
    "\n",
    "<h2 style=\"text-align: center;\"><a href=\"https://github.com/almond-sh/examples\">https://github.com/almond-sh/examples</a></h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Contributors Welcome!\n",
    "\n",
    "\n",
    "* We'd love to see\n",
    "  * More library/framework integrations\n",
    "  * More example notebooks\n",
    "  * Better IDE integration\n",
    "  * A logo :)\n",
    "\n",
    "<ul>\n",
    "    <li>Interested?\n",
    "        <ul style='list-style: none; margin-left: 0; '>\n",
    "            <li>&rArr; Talk to Alex (@alxarchambault) or me (@soebrunk)</li>\n",
    "            <li>&rArr; Join our Gitter channel: https://gitter.im/almond-sh/almond</li>\n",
    "            <li>&rArr; Or check out the almond repo on https://github.com/almond-sh/almond</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Recap\n",
    "\n",
    "<img src=\"img/learning-phases-only.svg\" width=\"40%\" style=\"margin: 4rem auto;\" />\n",
    "\n",
    "* Jupyter notebooks\n",
    " * Integrate **documentation**, **runnable code** and **rich output** in a single document\n",
    " * Combine REPLs and worksheets\n",
    "\n",
    "* **almond** combines the power of Jupyter and Ammonite\n",
    " * Giving us first class interactive computing support in Scala"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<div>\n",
    "    <h1 style=\"text-align: center; margin-top:20%; margin-bottom: 20%; font-size: 2.5em\">\n",
    "      Questions?</h1>\n",
    "</div>\n",
    "\n",
    "<img src=\"img/logo-katana.svg\" class=\"float-left\" width=\"20%\" />\n",
    "\n",
    "<p class=\"float-right\" style=\"text-align: right\">Sören Brunk<br>\n",
    "<img style=\"display: inline; height: 1em\" src=\"img/Twitter_bird_logo_2012.svg\"  /> @soebrunk</p>"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Scala",
   "language": "scala",
   "name": "scala"
  },
  "language_info": {
   "codemirror_mode": "text/x-scala",
   "file_extension": ".scala",
   "mimetype": "text/x-scala",
   "name": "scala",
   "nbconvert_exporter": "script",
   "version": "2.12.8"
  },
  "rise": {
   "autolaunch": true,
   "controls": true,
   "showSlideNumber": "speaker",
   "slideNumber": true,
   "theme": "solarized",
   "transition": "linear"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "370.594px"
   },
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}