How to render a Websequence diagram of Python code calls in Plantuml Jupyter Notebook

Websequence - Example Plantuml .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6375a639-07ad-48ca-a46a-c58c00140edc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "from plantuml import PlantUML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d5fe807-2794-47fa-8210-3ebace1c5a35",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = []\n",
    "\n",
    "# https://stackoverflow.com/a/60931048/1533001\n",
    "class SequenceOn:\n",
    "    autonumber = True\n",
    "    init_done = False\n",
    "\n",
    "    def __init__(self,participant=\"\"):\n",
    "\n",
    "        if not SequenceOn.init_done :\n",
    "            #activate if requested only once\n",
    "            if SequenceOn.autonumber: \n",
    "                text.append(\"autonumber\")\n",
    "\n",
    "            SequenceOn.init_done = True\n",
    "\n",
    "        #retrieve callee frame\n",
    "        callee_frame = sys._getframe(1)\n",
    "\n",
    "        #method/function name\n",
    "        self.__funcName = callee_frame.f_code.co_name\n",
    "\n",
    "        # look for a class name\n",
    "        if 'self' in callee_frame.f_locals:\n",
    "            self.__className = callee_frame.f_locals['self'].__class__.__name__\n",
    "        else:\n",
    "            self.__className = participant\n",
    "\n",
    "        #retrieve the caller frame and class name of the caller\n",
    "        caller_frame = sys._getframe(2)\n",
    "\n",
    "        if 'self' in caller_frame.f_locals:\n",
    "            self.__caller = caller_frame.f_locals['self'].__class__.__name__\n",
    "        else:\n",
    "            self.__caller = \"\"\n",
    "\n",
    "        #print the plantuml message\n",
    "        activate = \"++\" if self.__caller != self.__className else \"\"\n",
    "        text.append(f'{self.__caller} -> {self.__className} {activate} :{self.__funcName}')\n",
    "\n",
    "\n",
    "    def __del__(self):\n",
    "        ''' print the return message upon destruction '''\n",
    "        if self.__caller != self.__className:\n",
    "            text.append(f'{self.__caller} <-- {self.__className} -- ')\n",
    "\n",
    "    def note(self,msg):\n",
    "        text.append(f'note over {self.__className}:{msg}')\n",
    "\n",
    "class SequenceOff:\n",
    "    ''' empty class allowing to disable the trace by eg doing in the begining of a file:\n",
    "\n",
    "    Seq = SequenceOn # enable sequence generation\n",
    "    Seq = SequenceOff # disable sequence generation\n",
    "\n",
    "    and using seq for tracing instead of SequenceOn\n",
    "    '''\n",
    "    def __init__(self,participant=\"\"):\n",
    "        pass\n",
    "    def __call__(self,msg):\n",
    "        pass\n",
    "    def note(self,msg):\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d71d3cf3-c5ab-4189-8e06-3e16df236195",
   "metadata": {},
   "outputs": [],
   "source": [
    "class B:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "\n",
    "    def funcB1(self):\n",
    "        s = SequenceOn()\n",
    "\n",
    "    def funcB2(self):\n",
    "        s = SequenceOn()\n",
    "        s.note(\"calling Private method\")\n",
    "        self.__funcB22()\n",
    "\n",
    "    def __funcB22(self):\n",
    "        s = SequenceOn()\n",
    "\n",
    "class A:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    def funcA(self):\n",
    "\n",
    "        s = SequenceOn()\n",
    "        b = B()\n",
    "        b.funcB1()\n",
    "        b.funcB2()\n",
    "\n",
    "# simple sequence\n",
    "a = A()\n",
    "a.funcA()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "51c4d5c4-941e-419f-80c5-00a80bc2b8bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "\n",
    "content = PlantUML('http://www.plantuml.com/plantuml/img/').processes(\"\\n\".join(text))\n",
    "display(Image(content))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e83c363-441e-4a41-81ee-6811b881164e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}