{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Displaying information through the execution stack\n",
    "\n",
    "Plugins are very powerful and versatile objects that can be used for various applications.\n",
    "\n",
    "In this notebook we present a simple way to live-plot optimization processes via some Plugin.\n",
    "\n",
    "To demonstrate this, we will run a simple QAOA-MAXCUT algorithm using the QLM libraries and plot the trace during the optimization\n",
    "\n",
    "## Writing a widget plugin\n",
    "\n",
    "Lets write a simple widget plugin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-05T19:42:42.156084Z",
     "iopub.status.busy": "2022-11-05T19:42:42.155661Z",
     "iopub.status.idle": "2022-11-05T19:42:42.556169Z",
     "shell.execute_reply": "2022-11-05T19:42:42.555282Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "import hashlib  # To hash incoming jobs\n",
    "from qat.core.plugins import AbstractPlugin\n",
    "\n",
    "\n",
    "class WidgetPlugin(AbstractPlugin):\n",
    "    def __init__(self):\n",
    "        super(WidgetPlugin, self).__init__()\n",
    "        # Widget\n",
    "        self.line = None\n",
    "        self.fig = None\n",
    "        self.ax = None\n",
    "        # Data being drawn\n",
    "        self.x, self.y = None, None\n",
    "        \n",
    "    def reset(self):\n",
    "        \"\"\" Initializing a widget \"\"\"\n",
    "        self.x = []\n",
    "        self.y = []\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(1, 1, 1)\n",
    "        line, = ax.plot(self.x, self.y)\n",
    "        self.widget = line\n",
    "        self.fig = fig\n",
    "        self.ax = ax\n",
    "        self.fig.show()\n",
    "\n",
    "    def update_plot(self, value):\n",
    "        \"\"\" Updating the current plot\"\"\"\n",
    "        # Updating data\n",
    "        self.x.append(len(self.x))\n",
    "        self.y.append(value)\n",
    "        # Updating widget\n",
    "        self.widget.set_data(self.x, self.y)\n",
    "        # Updating axes limits\n",
    "        self.ax.set_xlim(( 0, len(self.x)))\n",
    "        self.ax.set_ylim((min(self.y) - 1e-1, max(self.y)+1e-1))\n",
    "        # Redrawing widget\n",
    "        self.fig.canvas.draw()\n",
    "\n",
    "    def compile(self, batch, _):\n",
    "        return batch\n",
    "\n",
    "    def post_process(self, batch_result):\n",
    "        self.update_plot(batch_result.results[0].value)\n",
    "        return batch_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing the tools we need from the QLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-05T19:42:42.559782Z",
     "iopub.status.busy": "2022-11-05T19:42:42.559414Z",
     "iopub.status.idle": "2022-11-05T19:42:43.014807Z",
     "shell.execute_reply": "2022-11-05T19:42:43.014078Z"
    }
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "from qat.plugins import ScipyMinimizePlugin\n",
    "from qat.vsolve.qaoa import MaxCut\n",
    "from qat.qpus import get_default_qpu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating a random instance of Max Cut (graph of size 6), Ansatz of depth 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-05T19:42:43.018412Z",
     "iopub.status.busy": "2022-11-05T19:42:43.018087Z",
     "iopub.status.idle": "2022-11-05T19:42:43.045241Z",
     "shell.execute_reply": "2022-11-05T19:42:43.044680Z"
    }
   },
   "outputs": [],
   "source": [
    "graph = nx.random_graphs.erdos_renyi_graph(6, 0.5)\n",
    "problem = MaxCut(graph)\n",
    "job = problem.to_job(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining our execution stack: \n",
    "* an optimizer\n",
    "* our widget plugin (to catch and plot the energy)\n",
    "* LinAlg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-05T19:42:43.048339Z",
     "iopub.status.busy": "2022-11-05T19:42:43.047829Z",
     "iopub.status.idle": "2022-11-05T19:42:43.051495Z",
     "shell.execute_reply": "2022-11-05T19:42:43.050975Z"
    }
   },
   "outputs": [],
   "source": [
    "display = WidgetPlugin()\n",
    "stack = ScipyMinimizePlugin(method=\"COBYLA\", tol=1e-3, options={\"maxiter\": 50}) | display | get_default_qpu()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calling the reset method sets up a plot, submitting a job will start plotting the energy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-05T19:42:43.054408Z",
     "iopub.status.busy": "2022-11-05T19:42:43.053960Z",
     "iopub.status.idle": "2022-11-05T19:42:43.096415Z",
     "shell.execute_reply": "2022-11-05T19:42:43.095882Z"
    }
   },
   "outputs": [],
   "source": [
    "display.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-05T19:42:43.112380Z",
     "iopub.status.busy": "2022-11-05T19:42:43.111957Z",
     "iopub.status.idle": "2022-11-05T19:42:45.343781Z",
     "shell.execute_reply": "2022-11-05T19:42:45.343231Z"
    }
   },
   "outputs": [],
   "source": [
    "result = stack.submit(job)"
   ]
  }
 ],
 "metadata": {
  "authors": [
   "Simon Martiel"
  ],
  "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": "3.10.4"
  },
  "tags": [
   "variational"
  ]
 },
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}