rapaio-jupyter-kernel-example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "copy of https://github.com/padreati/rapaio-jupyter-kernel/blob/2ce7a0c8dde6f35af2a276f14afff4ff998275f1/example.ipynb\n",
    "\n",
    "# Java Jupyter notebook example\n",
    "You can write normal Java."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "is_executing": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var result = 2 + 2;\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Complex[a=11.0, b=21.0]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "// including classes\n",
    "record Complex(double a, double b) {\n",
    "    public Complex add(Complex c) {\n",
    "        return new Complex(a+c.a, b+c.b);\n",
    "    }\n",
    "}\n",
    "Complex x = new Complex(10,20);\n",
    "x.add(new Complex(1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "// methods can also be implemented\n",
    "int add(int a, int b) { return a+b; }\n",
    "add(2,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Magic commands"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "Besides Java code, a cell can contain special commands implemented by the kernel. These are called magic code and there are two types: magic lines and magic cells. \n",
    "\n",
    "**Magic lines** are lines which are prefixed with `%`. After the prefix it is followed by the magic command and the optional parameters. Below is an example of magic line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0m\u001b[0m\u001b[1;30m|    record Complex\u001b[0m\u001b[0m\n"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "// magic line which asks JShell to list the types defined in this notebook in this moment\n",
    "%jshell /types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**Magic cells** are similar with magic lines, but it takes as parameters the following lines. Thus the magic cells should stay in a separate cells. Below is an example of magic cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distributor ID:\tUbuntu\n",
      "Description:\tUbuntu 22.04.4 LTS\n",
      "Release:\t22.04\n",
      "Codename:\tjammy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No LSB modules are available.\n"
     ]
    }
   ],
   "source": [
    "// call a bash command which lists the Linux variant that is used\n",
    "%%bash\n",
    "lsb_release -a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Magic commands interpolation\n",
    "\n",
    "Sometimes there is a need to run a magic command in a more dynamic way. This can be done using magic interpolation.\n",
    "\n",
    "Magic interpolation is the interpolation of marked content which starts with `\\{` and ends with `}`. Any content decorated with those markers is evaluated in jshell and the result is transformed in a String which replaces the decorated content in the magic command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "String version = \"1.0.2\";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding dependency \u001b[0m\u001b[1m\u001b[32mcom.github.javafaker:javafaker:1.0.2\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "%dependency /add com.github.javafaker:javafaker:\\{version}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "Magic interpolation can go with any content after `%` or `%%` like in the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "String module = \"dependency\";\n",
    "String cmd = \"/add\";\n",
    "String group = \"com.github.javafaker\";\n",
    "String artifact = \"javafaker\";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding dependency \u001b[0m\u001b[1m\u001b[32mcom.github.javafaker:javafaker:1.0.2\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "%\\{module} \\{cmd} \\{group}:\\{artifact}:\\{version}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "If one wants to avoid interpreting something like `\\{` not as an interpolation started than it should be escaped with another `\\`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "String types = \"/types\";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0m\u001b[0m\u001b[1;30m|    record Complex\u001b[0m\u001b[0m\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%jshell \\{types}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "MagicEvalException",
     "evalue": "Command not executed either because there is no handler or due to a syntax error.",
     "output_type": "error",
     "traceback": [
      "\u001b[0m\u001b[1m\u001b[31mMagicEvalException:\u001b[0m",
      "\u001b[0m\u001b[1m\u001b[34mCommand not executed either because there is no handler or due to a syntax error.\u001b[0m"
     ]
    }
   ],
   "source": [
    "%jshell \\\\{types}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependency management\n",
    "\n",
    "You can add dependencies using `%dependency /add` and after adding all dependencies you can call `%dependency /resolve`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding dependency \u001b[0m\u001b[1m\u001b[32mcom.github.javafaker:javafaker:1.0.2\n",
      "\u001b[0mSolving dependencies\n",
      "Resolved artifacts count: 5\n",
      "Add to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/com/github/javafaker/javafaker/1.0.2/javafaker-1.0.2.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/org/apache/commons/commons-lang3/3.5/commons-lang3-3.5.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/org/yaml/snakeyaml/1.23/snakeyaml-1.23-android.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/com/github/mifmif/generex/1.0.2/generex-1.0.2.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/dk/brics/automaton/automaton/1.11-8/automaton-1.11-8.jar\u001b[0m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "%dependency /add com.github.javafaker:javafaker:1.0.2\n",
    "%dependency /resolve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "When added you can import and use the dependency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hayley Anderson"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import com.github.javafaker.Faker;\n",
    "var faker = new Faker();\n",
    "faker.name().fullName()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resolving conflict dependencies\n",
    "\n",
    "You there are conflicts you can manage them with optional. Let's take an example which have conflicts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding dependency \u001b[0m\u001b[1m\u001b[32mcom.google.guava:guava:20.0\n",
      "\u001b[0mAdding dependency \u001b[0m\u001b[1m\u001b[32mcom.google.inject:guice:4.2.2\n",
      "\u001b[0mAdding dependency \u001b[0m\u001b[1m\u001b[32mcom.google.guava:guava:25.1-android\n",
      "\u001b[0mSolving dependencies\n",
      "Resolved artifacts count: 9\n",
      "Add to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/com/google/inject/guice/4.2.2/guice-4.2.2.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/javax/inject/javax.inject/1/javax.inject-1.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/aopalliance/aopalliance/1.0/aopalliance-1.0.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/com/google/guava/guava/25.1-android/guava-25.1-android.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/com/google/code/findbugs/jsr305/3.0.2/jsr305-3.0.2.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/org/checkerframework/checker-compat-qual/2.0.0/checker-compat-qual-2.0.0.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/com/google/errorprone/error_prone_annotations/2.1.3/error_prone_annotations-2.1.3.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/com/google/j2objc/j2objc-annotations/1.1/j2objc-annotations-1.1.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/org/codehaus/mojo/animal-sniffer-annotations/1.14/animal-sniffer-annotations-1.14.jar\u001b[0m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "%dependency /add com.google.guava:guava:20.0 --optional\n",
    "%dependency /add com.google.inject:guice:4.2.2\n",
    "%dependency /add com.google.guava:guava:25.1-android\n",
    "%dependency /resolve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Help on magic commands\n",
    "\n",
    "The magic `%help` provides more examples and guidance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0m\u001b[1m\u001b[34mInformation about registered magic handlers.\n",
       "\u001b[0m\u001b[0m\n",
       "\u001b[0m\u001b[1m\u001b[34mJShell commands\u001b[0m\u001b[0m\n",
       "\u001b[0m\u001b[1mDocumentation:\n",
       "\u001b[0m    Magic handler which runs command against JShell REPL and displays the results.\n",
       "    Not all JShell commands are implemented, since some of them does not make sense with notebooks (for example edit cell is handled simply by editing the corresponding code cell and run).\n",
       "\u001b[0m\u001b[1mSyntax:\n",
       "\u001b[0m    \u001b[0m\u001b[1m\u001b[32m%jshell /methods\u001b[0m\n",
       "    List all active methods.\n",
       "    \u001b[0m\u001b[1m\u001b[32m%jshell /vars\u001b[0m\n",
       "    List all active variables, with type and value.\n",
       "    \u001b[0m\u001b[1m\u001b[32m%jshell /imports\u001b[0m\n",
       "    List all active import statements.\n",
       "    \u001b[0m\u001b[1m\u001b[32m%jshell /types\u001b[0m\n",
       "    List all active types: classes, interfaces, enums and annotations.\n",
       "    \u001b[0m\u001b[1m\u001b[32m%jshell /list -all\u001b[0m\n",
       "    List all code snippets, either active, inactive or erroneous.\n",
       "    \u001b[0m\u001b[1m\u001b[32m%jshell /list [id]\u001b[0m\n",
       "    List snippet with the given id.\n",
       "    \u001b[0m\u001b[1m\u001b[32m%jshell /list\u001b[0m\n",
       "    List all active code snippets.\n",
       "\n",
       "\u001b[0m\u001b[1m\u001b[34mDependency manager\u001b[0m\u001b[0m\n",
       "\u001b[0m\u001b[1mDocumentation:\n",
       "\u001b[0m    Find and resolve a dependency using coordinates: group_id, artifact_id and version id.\n",
       "    The maven public repositories are searched for dependencies. Additionally, any maven transitive dependency declared with magic handlers are included in classpath.\n",
       "\u001b[0m\u001b[1mSyntax:\n",
       "\u001b[0m    \u001b[0m\u001b[1m\u001b[32m%dependency /list-repos\u001b[0m\n",
       "    List all repositories\n",
       "    \u001b[0m\u001b[1m\u001b[32m%dependency /add-repo name url[ release|update-policy]?[ snapshot|update-policy]?\u001b[0m\n",
       "    Add Maven Repository using a name and an url. Optional parameters are available. \n",
       "    Release enabled is obtained by specifying release|update-policy.\n",
       "    Update policy can be: never/always/daily/interval.\n",
       "    Snapshot enabled is obtained by specified snapshot|update-policy.\n",
       "    Update policy can be: never/always/daily/interval.\n",
       "    If any flag is not specified we have the following default options:\n",
       "     - release enabled with never, snapshot enabled with never for repos with url starting with file://\n",
       "     - release enabled with never, snapshot disabled for repos with url not starting with file://\n",
       "    \u001b[0m\u001b[1m\u001b[32m%dependency /list-dependencies\u001b[0m\n",
       "    List proposed and resolved dependencies\n",
       "    \u001b[0m\u001b[1m\u001b[32m%dependency /list-artifacts\u001b[0m\n",
       "    List artifacts loaded after the last dependency resolve\n",
       "    \u001b[0m\u001b[1m\u001b[32m%dependency /resolve\u001b[0m\n",
       "    Resolve dependencies\n",
       "    \u001b[0m\u001b[1m\u001b[32m%dependency /add groupId:artifactId[:extension[:classifier]]:version\u001b[0m\n",
       "    \u001b[0m\u001b[1m\u001b[32m%dependency /add groupId:artifactId[:extension[:classifier]]:version --optional\u001b[0m\n",
       "    Declares a dependency to the current notebook. Artifact's extension (default value jar) and classifier (default value is empty string) values are optional.\n",
       "\n",
       "\u001b[0m\u001b[1m\u001b[34mJar magic handler\u001b[0m\u001b[0m\n",
       "\u001b[0m\u001b[1mDocumentation:\n",
       "\u001b[0m    Jar magic handler allows adding to the current classpath a local jar file or all the jar files from a given directory.\n",
       "\u001b[0m\u001b[1mSyntax:\n",
       "\u001b[0m    \u001b[0m\u001b[1m\u001b[32m%jar path_to_jar_or_folder_of_jars\u001b[0m\n",
       "    Adds to the classpath a jar or all jar archives from a directory\n",
       "    \u001b[0m\u001b[1m\u001b[32m%%jars\u001b[0m\n",
       "    \u001b[0m\u001b[1m\u001b[32mjar_file_or_folder_1\u001b[0m\n",
       "    \u001b[0m\u001b[1m\u001b[32m[jar_file_or_folder_2]\u001b[0m\n",
       "    \u001b[0m\u001b[1m\u001b[32m...\u001b[0m\n",
       "    \u001b[0m\u001b[1m\u001b[32m[jar_file_or_folder_n]\u001b[0m\n",
       "    Adds to classpath all referenced jars or all jar files from referenced directories.\n",
       "\n",
       "\u001b[0m\u001b[1m\u001b[34mLoad\u001b[0m\u001b[0m\n",
       "\u001b[0m\u001b[1mDocumentation:\n",
       "\u001b[0m    Loads and executes a local jshell script file or a jupyter notebook for java language.\n",
       "\u001b[0m\u001b[1mSyntax:\n",
       "\u001b[0m    \u001b[0m\u001b[1m\u001b[32m%load path_to_script_or_notebook\u001b[0m\n",
       "    \n",
       "\n",
       "\u001b[0m\u001b[1m\u001b[34mImage\u001b[0m\u001b[0m\n",
       "\u001b[0m\u001b[1mDocumentation:\n",
       "\u001b[0m    Displays an image from a file or URL.\n",
       "\u001b[0m\u001b[1mSyntax:\n",
       "\u001b[0m    \u001b[0m\u001b[1m\u001b[32m%image path_to_file_or_url\u001b[0m\n",
       "    Display an image from a local file or from an URL\n",
       "\n",
       "\u001b[0m\u001b[1m\u001b[34mClasspath\u001b[0m\u001b[0m\n",
       "\u001b[0m\u001b[1mDocumentation:\n",
       "\u001b[0m    Magic handler which allows one to add to classpath directories with compiled code.\n",
       "\u001b[0m\u001b[1mSyntax:\n",
       "\u001b[0m    \u001b[0m\u001b[1m\u001b[32m%classpath path_to_folder_with_classes\u001b[0m\n",
       "    Adds to classpath folder which contains class files\n",
       "\n",
       "\u001b[0m\u001b[1m\u001b[34mBash\u001b[0m\u001b[0m\n",
       "\u001b[0m\u001b[1mDocumentation:\n",
       "\u001b[0m    Executes command line in a bash shell.\n",
       "\u001b[0m\u001b[1mSyntax:\n",
       "\u001b[0m    \u001b[0m\u001b[1m\u001b[32m%%bash\u001b[0m\n",
       "    \u001b[0m\u001b[1m\u001b[32mbash script\u001b[0m\n",
       "    Executes the following lines in a terminal.\n",
       "\n",
       "\u001b[0m\u001b[1m\u001b[34mHelp\u001b[0m\u001b[0m\n",
       "\u001b[0m\u001b[1mDocumentation:\n",
       "\u001b[0m    Magic which displays help for all the magic tools.\n",
       "\u001b[0m\u001b[1mSyntax:\n",
       "\u001b[0m    \u001b[0m\u001b[1m\u001b[32m%help\u001b[0m\n",
       "    Display help for all magic handlers.\n"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%help"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## JShell commands\n",
    "\n",
    "Some JShell commands are implemented. For example you can inspect which variables are defined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0m|    \u001b[1mint \u001b[32mresult\u001b[0m = \u001b[0m4\n",
       "\u001b[0m\u001b[0m|    \u001b[1mComplex \u001b[32mx\u001b[0m = \u001b[0mComplex[a=10.0, b=20.0]\n",
       "\u001b[0m\u001b[0m|    \u001b[1mComplex \u001b[32m$1\u001b[0m = \u001b[0mComplex[a=11.0, b=21.0]\n",
       "\u001b[0m\u001b[0m|    \u001b[1mint \u001b[32m$2\u001b[0m = \u001b[0m5\n",
       "\u001b[0m\u001b[0m|    \u001b[1mString \u001b[32mversion\u001b[0m = \u001b[0m\"1.0.2\"\n",
       "\u001b[0m\u001b[0m|    \u001b[1mString \u001b[32mmodule\u001b[0m = \u001b[0m\"dependency\"\n",
       "\u001b[0m\u001b[0m|    \u001b[1mString \u001b[32mcmd\u001b[0m = \u001b[0m\"/add\"\n",
       "\u001b[0m\u001b[0m|    \u001b[1mString \u001b[32mgroup\u001b[0m = \u001b[0m\"com.github.javafaker\"\n",
       "\u001b[0m\u001b[0m|    \u001b[1mString \u001b[32martifact\u001b[0m = \u001b[0m\"javafaker\"\n",
       "\u001b[0m\u001b[0m|    \u001b[1mString \u001b[32mtypes\u001b[0m = \u001b[0m\"/types\"\n",
       "\u001b[0m\u001b[0m|    \u001b[1mFaker \u001b[32mfaker\u001b[0m = \u001b[0mcom.github.javafaker.Faker@2ff0cf6f\n",
       "\u001b[0m\u001b[0m|    \u001b[1mString \u001b[32m$3\u001b[0m = \u001b[0m\"Hayley Anderson\"\n",
       "\u001b[0m"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%jshell /vars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "or the types you defined in this session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0m\u001b[0m\u001b[1;30m|    record Complex\u001b[0m\u001b[0m\n"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%jshell /types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Execute bash commands\n",
    "You can execute bash scripting commands. Here we display the java version number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "openjdk 22.0.2 2024-07-16\n",
      "OpenJDK Runtime Environment Corretto-22.0.2.9.1 (build 22.0.2+9-FR)\n",
      "OpenJDK 64-Bit Server VM Corretto-22.0.2.9.1 (build 22.0.2+9-FR, mixed mode, sharing)\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "java --version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "You can even define variables. In fact all the lines below cell magic marker are executed as a bash script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello John\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "name=\"John\"\n",
    "echo \"Hello $name\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Show an image for immediate inspection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%image https://www.google.com/logos/doodles/2024/paris-games-sailing-6753651837110529.4-law.gif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# Display data\n",
    "\n",
    "Jupyter notebooks uses outputs to display objects of various types. By default when an object is returned as the result of the last code operation, that result is displayed. \n",
    "\n",
    "The object which is displayed can be anything. If the object has a display handler registered, than that renderer is used to transform the object into a displayable content. If there is no registered display handler than the object is transformed into a string and that will be displayed.\n",
    "\n",
    "Previously we used magic commands to display an image. However for BufferedImages there is a registered handler and if you obtain an instance of a BufferedImage it will be displayed properly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "645a1759-cd78-462d-9005-002f685bdd1b"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import javax.imageio.*;\n",
    "display(ImageIO.read(new URL(\"https://www.google.com/logos/doodles/2024/paris-games-sailing-6753651837110529.4-law.gif\")));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Displayed data has a mime type. You can use that to describe how the object should be interpreted. For example we display a markdown snippet and we direct the output interpretation of the snippet through MIME type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Markdown *test* **snippet**:\n",
       "* bullet 1\n",
       "* bullet 2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "d3f8cd31-db09-42ac-8612-f91353e57edf"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(\"text/markdown\", \"Markdown *test* **snippet**:\\n* bullet 1\\n* bullet 2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "`display` command returns an id which identifies the piece of output from the notebook which handles the display. Notice that we captured the id of the display. This id can be used to update the same display with a different content. For example we can update the content of that display with a html snippet, using the MIME type for interpretation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "Html <i>test</i> <b>snippet</b>:<p><ulist><li>bullet 1</li><li>bullet 2</li></ulist></p>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "String id = display(\"text/markdown\", \"Markdown *test* **snippet**:\\n* bullet 1\\n* bullet 2\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "updateDisplay(id, \"text/html\", \n",
    "              \"Html <i>test</i> <b>snippet</b>:<p><ulist><li>bullet 1</li><li>bullet 2</li></ulist></p>\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Java object is displayed as a String using *Objects.toString*. As such, if the object has an implementation of *toString*, that method will be called."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Complex[a=10.0, b=3.141592653589793]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new Complex(10,Math.PI);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous happens because record objects have by default an implementation of *toString* method. This is not true with other objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "javax.imageio.ImageReadParam@4ab63989"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new ImageReadParam()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous outputs the standard way of Java to display objects, aka using the type and the numeric reference to that object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# Using graph libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "If you want to analyze data you perhaps need a graphical library. There are some notable options. A simple one is to use *xchart* library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding dependency \u001b[0m\u001b[1m\u001b[32morg.knowm.xchart:xchart:3.8.8\n",
      "\u001b[0mSolving dependencies\n",
      "Resolved artifacts count: 16\n",
      "Add to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/org/knowm/xchart/xchart/3.8.8/xchart-3.8.8.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/de/erichseifert/vectorgraphics2d/VectorGraphics2D/0.13/VectorGraphics2D-0.13.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/de/rototor/pdfbox/graphics2d/3.0.1/graphics2d-3.0.1.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/org/apache/pdfbox/pdfbox/3.0.1/pdfbox-3.0.1.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/org/apache/pdfbox/pdfbox-io/3.0.1/pdfbox-io-3.0.1.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/org/apache/pdfbox/fontbox/3.0.1/fontbox-3.0.1.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/commons-logging/commons-logging/1.2/commons-logging-1.2.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/org/junit/jupiter/junit-jupiter/5.10.1/junit-jupiter-5.10.1.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/org/junit/jupiter/junit-jupiter-api/5.10.1/junit-jupiter-api-5.10.1.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/org/opentest4j/opentest4j/1.3.0/opentest4j-1.3.0.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/org/junit/platform/junit-platform-commons/1.10.1/junit-platform-commons-1.10.1.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/org/apiguardian/apiguardian-api/1.1.2/apiguardian-api-1.1.2.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/org/junit/jupiter/junit-jupiter-params/5.10.1/junit-jupiter-params-5.10.1.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/org/junit/jupiter/junit-jupiter-engine/5.10.1/junit-jupiter-engine-5.10.1.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/org/junit/platform/junit-platform-engine/1.10.1/junit-platform-engine-1.10.1.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/com/madgag/animated-gif-lib/1.4/animated-gif-lib-1.4.jar\u001b[0m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "%dependency /add org.knowm.xchart:xchart:3.8.8\n",
    "%dependency /resolve\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import org.knowm.xchart.*;\n",
    "\n",
    "var x = new double[] {1,2,3,4,5,6};\n",
    "var y = new double[] {3,2,3,1,4,6};\n",
    "var chart = QuickChart.getChart(\"Test\", \"x\", \"y\", \"f\", x, y);\n",
    "BitmapEncoder.getBufferedImage(chart);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Other options include *JFreeChart*, *chart4j* and others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "The main reason why I contribute to this project is to have a easy to use kernel to play with my statistics and machine learning library: *rapaio*. This library is also an option for experimenting with graphical content on data analysis. Below is an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding dependency \u001b[0m\u001b[1m\u001b[32mio.github.padreati:rapaio-core:6.1.0\n",
      "\u001b[0mSolving dependencies\n",
      "Resolved artifacts count: 2\n",
      "Add to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/io/github/padreati/rapaio-core/6.1.0/rapaio-core-6.1.0.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/home/ati/work/rapaio-jupyter-kernel/target/mima_cache/io/github/padreati/rapaio-code-gen/6.1.0/rapaio-code-gen-6.1.0.jar\u001b[0m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "%dependency /add io.github.padreati:rapaio-core:6.1.0\n",
    "%dependency /resolve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import rapaio.data.*;\n",
    "import rapaio.core.distributions.*;\n",
    "import rapaio.graphics.plot.*;\n",
    "import rapaio.graphics.plot.artist.*;\n",
    "import static rapaio.graphics.Plotter.*;\n",
    "import static rapaio.graphics.opt.GOpts.*;\n",
    "import java.awt.*;\n",
    "import static rapaio.sys.WS.*;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var x = VarDouble.sample(Normal.of(0, 2), 1_000);\n",
    "image(hist(x).xLab(\"x\").yLab(\"Freq\").title(\"Normal distribution sample\"), 800, 400);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var x = VarDouble.seq(0, 1, 0.004).name(\"x\");\n",
    "\n",
    "Color cgreen = Color.decode(\"0x2ca02c\");\n",
    "Color cblue = Color.decode(\"0x1f77b4\");\n",
    "Color corange = Color.decode(\"0xff7f0e\");\n",
    "Color cred = Color.decode(\"0xd62728\");\n",
    "\n",
    "var ygreen = VarDouble.from(x, Normal.of(0.24, 0.08)::pdf).name(\"y\");\n",
    "var yblue = VarDouble.from(x, Normal.of(0.37, 0.15)::pdf).name(\"y\");\n",
    "var yorange = VarDouble.from(x, Normal.of(0.59, 0.13)::pdf).name(\"y\");\n",
    "var yred = VarDouble.from(x, Normal.of(0.80, 0.06)::pdf).name(\"y\");\n",
    "\n",
    "float alpha = 0.5f;\n",
    "float lwd = 5f;\n",
    "\n",
    "Plot up = plot();\n",
    "\n",
    "up.add(new PolyFill(x, yblue, fill(cblue), alpha(alpha)));\n",
    "up.add(new PolyFill(x, yorange, fill(corange), alpha(alpha)));\n",
    "up.add(new PolyFill(x, ygreen, fill(cgreen), alpha(alpha)));\n",
    "up.add(new PolyFill(x, yred, fill(cred), alpha(alpha)));\n",
    "\n",
    "up.add(new PolyLine(false, x, yblue, color(cblue), lwd(lwd)));\n",
    "up.add(new PolyLine(false, x, yorange, color(corange), lwd(lwd)));\n",
    "up.add(new PolyLine(false, x, ygreen, color(cgreen), lwd(lwd)));\n",
    "up.add(new PolyLine(false, x, yred, color(cred), lwd(lwd)));\n",
    "\n",
    "up.xLim(0, 1);\n",
    "up.leftThick(false);\n",
    "up.leftMarkers(false);\n",
    "up.bottomThick(false);\n",
    "up.bottomMarkers(false);\n",
    "\n",
    "Plot down = plot();\n",
    "\n",
    "down.leftThick(false);\n",
    "down.leftMarkers(false);\n",
    "down.bottomThick(false);\n",
    "down.bottomMarkers(false);\n",
    "\n",
    "down.add(new Text(0.5, 0.6, \"rapaio\", font(\"DejaVu Sans\", Font.BOLD, 80),\n",
    "        halign.center(), valign.center(), color(Color.decode(\"0x096b87\"))));\n",
    "down.xLim(0, 1);\n",
    "down.yLim(0, 1);\n",
    "\n",
    "image(gridLayer(2, 1, heights(0.7, 0.3)).add(up).add(down), 300, 300);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "For a detailed example of using *rapaio* library a dedicated notebook will be presented. In the meantime you can explore the notebook documentation of the library [Rapaio - Data Visualization](https://padreati.github.io/rapaio/tutorials/DataVisualization/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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