{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving & loading jobs\n",
    "This example demonstrates how jobs can be saved to files and then recovered later, allowing for the retrieval of results between sessions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightworks as lw\n",
    "from lightworks import remote\n",
    "\n",
    "try:\n",
    "    remote.token.load(\"main_token\")\n",
    "except remote.TokenError:\n",
    "    print(\n",
    "        \"Token could not be automatically loaded, this will need to be \"\n",
    "        \"manually configured.\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll start by generating a random task to submit to the system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "qpu = remote.QPU(\"Artemis\")\n",
    "\n",
    "circuit = lw.Unitary(lw.random_unitary(6))\n",
    "input_state = lw.State([1, 0, 0, 1, 0, 0])\n",
    "n_samples = 1000\n",
    "\n",
    "sampler = lw.Sampler(circuit, input_state, n_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then run this on the target QPU to generate a job. The ID of this can then be printed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job ID: 17769\n"
     ]
    }
   ],
   "source": [
    "job = qpu.run(sampler)\n",
    "print(f\"Job ID: {job.job_id}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving\n",
    "Once a job is created, this can then be saved as a json file using the provided method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "job.save_to_file(\"demo_job\", allow_overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading\n",
    "To recover a job, the load_job_from_file method is used, providing the name of the file to it. It can be seen how the job ID is correctly recovered after saving."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job ID: 17769\n"
     ]
    }
   ],
   "source": [
    "loaded_job = remote.load_job_from_file(\"demo_job\")\n",
    "print(f\"Job ID: {loaded_job.job_id}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The return of this method is a Job, which can be used in the same way as any other job. For example, results can be downloaded and plotting using the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loaded_job.wait_until_complete()\n",
    "\n",
    "results = loaded_job.get_result()\n",
    "results.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}