{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9dac5cbe-2f50-4d12-834a-a0f5c3fca75d",
   "metadata": {},
   "source": [
    "# Create Notebooks but not run them\n",
    "\n",
    "One should check [how-to-run-notebook-on-cluster.ipynb](../how-to-run-notebook-on-cluster.ipynb) for executing the notebooks on a cluster.\n",
    "For here, we only generate evaluation notebooks **if they do not exist**.\n",
    "Thus, you can effectively force a rerun, or update the notebook to the latest version, by removing the old notebooks.\n",
    "You can also choose to remove the JSON data or not, depending on your needs.\n",
    "\n",
    "## Tips\n",
    "\n",
    "When you first run some notebook from scratch, try to use a small number of workers (e.g., `slurm_maximum_jobs=30`).\n",
    "This is because normally at the beginning of the evaluation, the jobs are highly sequential.\n",
    "At this stage, the submitter and the executor are still trying to figure out how fast each shot runs and how many shots we need to gather.\n",
    "They don't want to submit too much jobs that it takes forever to execute.\n",
    "After it gathers enough data, e.g., has gone to the next (higher precision) submitter, then you can increase the number of workers because those jobs are highly parallelizable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c795c9a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fb35a986",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "available keys\n",
      "    - decoder_eval: str = '\"mwpm\"'\n",
      "    - code_eval: str = 'f\"rsc(d@{c.d},p@{p})\"'\n",
      "    - noise_eval: str = '\"none\"'\n",
      "    - config_vec_eval: str = '[dmap(d@3), dmap(d@5), dmap(d@7), dmap(d@9), dmap(d@11)]'\n",
      "    - p_center: float = 0.01\n",
      "    - per10_p_count: int = 3\n",
      "    - p_bias: int = 0\n",
      "    - slurm_maximum_jobs: None = 30\n",
      "    - slurm_cores_per_node: int = 10\n",
      "    - slurm_mem_per_job: int = 2\n",
      "    - slurm_walltime: str = \"1-00:00:00\"\n",
      "    - local_maximum_jobs: None = 10\n",
      "    - force_finished: bool = False\n"
     ]
    }
   ],
   "source": [
    "import os, sys, time\n",
    "import papermill as pm\n",
    "from dotmap import DotMap as dmap\n",
    "from slugify import slugify\n",
    "from qec_lego_bench.hpc.cluster_util import tmux_list_script\n",
    "import nbstripout  # please install, and we will call it from command line\n",
    "import subprocess\n",
    "\n",
    "template_path = 'template.ipynb'\n",
    "\n",
    "inspected = pm.inspect_notebook(template_path)\n",
    "print(\"available keys\")\n",
    "for key, value in inspected.items():\n",
    "    v = dmap(value)\n",
    "    print(f\"    - {key}: {v.inferred_type_name} = {v.default}\")\n",
    "\n",
    "# remove all the output from the notebook\n",
    "def remove_notebook_output(nb_path: str):\n",
    "    subprocess.Popen(f\"nbstripout {nb_path}\", shell=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91cda08c",
   "metadata": {},
   "source": [
    "## Rotated Surface Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dbd6b87f-d9bf-4d02-a6a7-504559262b15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "config_vec_eval: [dmap(d@3),dmap(d@5),dmap(d@7),dmap(d@9),dmap(d@11)]\n",
      "skip existing: f-rsc-d-c-d-p-p_none/mwpm.ipynb\n",
      "skip existing: f-rsc-d-c-d-p-p_none/mwpf.ipynb\n",
      "skip existing: f-rsc-d-c-d-p-p_none/huf.ipynb\n",
      "skip existing: f-rsc-d-c-d-p-p_none/f-mwpf-c-50-c-d.ipynb\n",
      "skip existing: f-rsc-d-c-d-p-p_none/bposd.ipynb\n",
      "skip existing: f-rsc-d-c-d-p-p_none/bposd-max-iter-5.ipynb\n",
      "skip existing: f-rsc-d-c-d-p-p_none/bposd-max-iter-5-osd-order-10-osd-method-osd-e.ipynb\n",
      "skip existing: f-rsc-d-c-d-p-p_none/bpuf.ipynb\n",
      "skip existing: f-rsc-d-c-d-p-p_none/bpuf-max-iter-5.ipynb\n",
      "skip existing: f-rsc-d-c-d-p-p_none/run_all.sh\n"
     ]
    }
   ],
   "source": [
    "d_vec = [3, 5, 7, 9, 11]\n",
    "\n",
    "code_parameters = dict(\n",
    "    code_eval = 'f\"rsc(d@{c.d},p@{p})\"',\n",
    "    noise_eval = '\"none\"',\n",
    "    p_center = 0.01,\n",
    "    per10_p_count = 3,\n",
    "    p_bias = -2,  # 0.0022\n",
    "    slurm_mem_per_job = 4,\n",
    "    config_vec_eval=\"[\" + \",\".join(f\"dmap(d@{d})\" for d in d_vec) + \"]\",\n",
    "    slurm_maximum_jobs=30,  # smaller instances for kick starts\n",
    ")\n",
    "\n",
    "print(\"config_vec_eval:\", code_parameters[\"config_vec_eval\"])\n",
    "\n",
    "folder = slugify(code_parameters[\"code_eval\"]) + \"_\" + slugify(code_parameters[\"noise_eval\"])\n",
    "os.makedirs(folder, exist_ok=True)\n",
    "\n",
    "decoder_eval_vec = [\n",
    "    '\"mwpm\"',\n",
    "    '\"mwpf\"',\n",
    "    '\"huf\"',\n",
    "    'f\"mwpf(c@{50*c.d})\"',  # auto scaling\n",
    "    '\"bposd\"',\n",
    "    '\"bposd(max_iter@5)\"',\n",
    "    '\"bposd(max_iter@5,osd_order@10,osd_method@osd_e)\"',\n",
    "    '\"bpuf\"',\n",
    "    '\"bpuf(max_iter@5)\"',\n",
    "]\n",
    "\n",
    "jobs: list[tuple[str, str]] = []\n",
    "for decoder_eval in decoder_eval_vec:\n",
    "    parameters = dict(\n",
    "        **code_parameters,\n",
    "        decoder_eval=decoder_eval,\n",
    "    )\n",
    "    notebook_filename = slugify(decoder_eval) + \".ipynb\"\n",
    "    notebook_path = os.path.join(folder, notebook_filename)\n",
    "    if os.path.exists(notebook_path):\n",
    "        print(\"skip existing:\", notebook_path)\n",
    "    else:\n",
    "        print(\"parameters:\", parameters)\n",
    "        pm.execute_notebook(\n",
    "            template_path,\n",
    "            notebook_path,\n",
    "            parameters=parameters,\n",
    "            prepare_only=True,  # do not run the notebook\n",
    "        )\n",
    "        remove_notebook_output(notebook_path)\n",
    "    # create tmux session to run these commands\n",
    "    tmux_name = slugify(decoder_eval) + \"_\" + slugify(code_parameters[\"code_eval\"]) + \"_\" + slugify(code_parameters[\"noise_eval\"])\n",
    "    command = f\"srun --time=23:55:00 --mem=10G --cpus-per-task=2 papermill {notebook_filename} ${{RID}}.{notebook_filename}\"\n",
    "    jobs.append((tmux_name, command))\n",
    "\n",
    "script_path = os.path.join(folder, \"run_all.sh\")\n",
    "script = tmux_list_script(jobs, prefix=\"RID=zz-3\")\n",
    "if os.path.exists(script_path):\n",
    "    print(\"skip existing:\", script_path)\n",
    "else:\n",
    "    with open(script_path, \"w\") as f:\n",
    "        f.write(script)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b44c8430",
   "metadata": {},
   "source": [
    "## Un-rotated Surface Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f164fad2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "config_vec_eval: [dmap(d@3),dmap(d@5),dmap(d@7),dmap(d@9),dmap(d@11)]\n",
      "skip existing: f-usc-d-c-d-p-p_none/mwpm.ipynb\n",
      "skip existing: f-usc-d-c-d-p-p_none/mwpf.ipynb\n",
      "skip existing: f-usc-d-c-d-p-p_none/huf.ipynb\n",
      "skip existing: f-usc-d-c-d-p-p_none/f-mwpf-c-50-c-d.ipynb\n",
      "skip existing: f-usc-d-c-d-p-p_none/bposd.ipynb\n",
      "skip existing: f-usc-d-c-d-p-p_none/bposd-max-iter-5.ipynb\n",
      "skip existing: f-usc-d-c-d-p-p_none/bposd-max-iter-5-osd-order-10-osd-method-osd-e.ipynb\n",
      "skip existing: f-usc-d-c-d-p-p_none/bpuf.ipynb\n",
      "skip existing: f-usc-d-c-d-p-p_none/bpuf-max-iter-5.ipynb\n",
      "skip existing: f-usc-d-c-d-p-p_none/run_all.sh\n"
     ]
    }
   ],
   "source": [
    "d_vec = [3, 5, 7, 9, 11]\n",
    "\n",
    "code_parameters = dict(\n",
    "    code_eval = 'f\"usc(d@{c.d},p@{p})\"',\n",
    "    noise_eval = '\"none\"',\n",
    "    p_center = 0.01,\n",
    "    per10_p_count = 3,\n",
    "    p_bias = -1,  # 0.0046\n",
    "    slurm_mem_per_job = 4,\n",
    "    config_vec_eval=\"[\" + \",\".join(f\"dmap(d@{d})\" for d in d_vec) + \"]\",\n",
    "    slurm_maximum_jobs=30,  # smaller instances for kick starts\n",
    ")\n",
    "\n",
    "print(\"config_vec_eval:\", code_parameters[\"config_vec_eval\"])\n",
    "\n",
    "folder = slugify(code_parameters[\"code_eval\"]) + \"_\" + slugify(code_parameters[\"noise_eval\"])\n",
    "os.makedirs(folder, exist_ok=True)\n",
    "\n",
    "decoder_eval_vec = [\n",
    "    '\"mwpm\"',\n",
    "    '\"mwpf\"',\n",
    "    '\"huf\"',\n",
    "    'f\"mwpf(c@{50*c.d})\"',  # auto scaling\n",
    "    '\"bposd\"',\n",
    "    '\"bposd(max_iter@5)\"',\n",
    "    '\"bposd(max_iter@5,osd_order@10,osd_method@osd_e)\"',\n",
    "    '\"bpuf\"',\n",
    "    '\"bpuf(max_iter@5)\"',\n",
    "]\n",
    "\n",
    "jobs: list[tuple[str, str]] = []\n",
    "for decoder_eval in decoder_eval_vec:\n",
    "    parameters = dict(\n",
    "        **code_parameters,\n",
    "        decoder_eval=decoder_eval,\n",
    "    )\n",
    "    notebook_filename = slugify(decoder_eval) + \".ipynb\"\n",
    "    notebook_path = os.path.join(folder, notebook_filename)\n",
    "    if os.path.exists(notebook_path):\n",
    "        print(\"skip existing:\", notebook_path)\n",
    "    else:\n",
    "        print(\"parameters:\", parameters)\n",
    "        pm.execute_notebook(\n",
    "            template_path,\n",
    "            notebook_path,\n",
    "            parameters=parameters,\n",
    "            prepare_only=True,  # do not run the notebook\n",
    "        )\n",
    "        remove_notebook_output(notebook_path)\n",
    "    # create tmux session to run these commands\n",
    "    tmux_name = slugify(decoder_eval) + \"_\" + slugify(code_parameters[\"code_eval\"]) + \"_\" + slugify(code_parameters[\"noise_eval\"])\n",
    "    command = f\"srun --time=23:55:00 --mem=10G --cpus-per-task=2 papermill {notebook_filename} ${{RID}}.{notebook_filename}\"\n",
    "    jobs.append((tmux_name, command))\n",
    "\n",
    "script_path = os.path.join(folder, \"run_all.sh\")\n",
    "script = tmux_list_script(jobs, prefix=\"RID=zz-3\")\n",
    "if os.path.exists(script_path):\n",
    "    print(\"skip existing:\", script_path)\n",
    "else:\n",
    "    with open(script_path, \"w\") as f:\n",
    "        f.write(script)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b15804",
   "metadata": {},
   "source": [
    "## Repetition Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9ba00612",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "config_vec_eval: [dmap(d@3),dmap(d@5),dmap(d@7),dmap(d@9),dmap(d@11),dmap(d@13),dmap(d@15)]\n",
      "skip existing: f-rep-d-c-d-p-p_none/mwpm.ipynb\n",
      "skip existing: f-rep-d-c-d-p-p_none/mwpf.ipynb\n",
      "skip existing: f-rep-d-c-d-p-p_none/huf.ipynb\n",
      "skip existing: f-rep-d-c-d-p-p_none/f-mwpf-c-50-c-d.ipynb\n",
      "skip existing: f-rep-d-c-d-p-p_none/bposd.ipynb\n",
      "skip existing: f-rep-d-c-d-p-p_none/bposd-max-iter-5.ipynb\n",
      "skip existing: f-rep-d-c-d-p-p_none/bposd-max-iter-5-osd-order-10-osd-method-osd-e.ipynb\n",
      "skip existing: f-rep-d-c-d-p-p_none/bpuf.ipynb\n",
      "skip existing: f-rep-d-c-d-p-p_none/bpuf-max-iter-5.ipynb\n",
      "skip existing: f-rep-d-c-d-p-p_none/run_all.sh\n"
     ]
    }
   ],
   "source": [
    "d_vec = [3, 5, 7, 9, 11, 13, 15]\n",
    "\n",
    "code_parameters = dict(\n",
    "    code_eval = 'f\"rep(d@{c.d},p@{p})\"',\n",
    "    noise_eval = '\"none\"',\n",
    "    p_center = 0.1,\n",
    "    per10_p_count = 3,\n",
    "    p_bias = -2,  # 0.022\n",
    "    slurm_mem_per_job = 4,\n",
    "    config_vec_eval=\"[\" + \",\".join(f\"dmap(d@{d})\" for d in d_vec) + \"]\",\n",
    "    slurm_maximum_jobs=30,  # smaller instances for kick starts\n",
    ")\n",
    "\n",
    "print(\"config_vec_eval:\", code_parameters[\"config_vec_eval\"])\n",
    "\n",
    "folder = slugify(code_parameters[\"code_eval\"]) + \"_\" + slugify(code_parameters[\"noise_eval\"])\n",
    "os.makedirs(folder, exist_ok=True)\n",
    "\n",
    "decoder_eval_vec = [\n",
    "    '\"mwpm\"',\n",
    "    '\"mwpf\"',\n",
    "    '\"huf\"',\n",
    "    'f\"mwpf(c@{50*c.d})\"',  # auto scaling\n",
    "    '\"bposd\"',\n",
    "    '\"bposd(max_iter@5)\"',\n",
    "    '\"bposd(max_iter@5,osd_order@10,osd_method@osd_e)\"',\n",
    "    '\"bpuf\"',\n",
    "    '\"bpuf(max_iter@5)\"',\n",
    "]\n",
    "\n",
    "jobs: list[tuple[str, str]] = []\n",
    "for decoder_eval in decoder_eval_vec:\n",
    "    parameters = dict(\n",
    "        **code_parameters,\n",
    "        decoder_eval=decoder_eval,\n",
    "    )\n",
    "    notebook_filename = slugify(decoder_eval) + \".ipynb\"\n",
    "    notebook_path = os.path.join(folder, notebook_filename)\n",
    "    if os.path.exists(notebook_path):\n",
    "        print(\"skip existing:\", notebook_path)\n",
    "    else:\n",
    "        print(\"parameters:\", parameters)\n",
    "        pm.execute_notebook(\n",
    "            template_path,\n",
    "            notebook_path,\n",
    "            parameters=parameters,\n",
    "            prepare_only=True,  # do not run the notebook\n",
    "        )\n",
    "        remove_notebook_output(notebook_path)\n",
    "    # create tmux session to run these commands\n",
    "    tmux_name = slugify(decoder_eval) + \"_\" + slugify(code_parameters[\"code_eval\"]) + \"_\" + slugify(code_parameters[\"noise_eval\"])\n",
    "    command = f\"srun --time=23:55:00 --mem=10G --cpus-per-task=2 papermill {notebook_filename} ${{RID}}.{notebook_filename}\"\n",
    "    jobs.append((tmux_name, command))\n",
    "\n",
    "script_path = os.path.join(folder, \"run_all.sh\")\n",
    "script = tmux_list_script(jobs, prefix=\"RID=zz-3\")\n",
    "if os.path.exists(script_path):\n",
    "    print(\"skip existing:\", script_path)\n",
    "else:\n",
    "    with open(script_path, \"w\") as f:\n",
    "        f.write(script)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94f77e26",
   "metadata": {},
   "source": [
    "## Color Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "488a1183",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "config_vec_eval: [dmap(d@3),dmap(d@5),dmap(d@7),dmap(d@9),dmap(d@11)]\n",
      "skip existing: f-color-d-c-d-p-p_none/mwpf.ipynb\n",
      "skip existing: f-color-d-c-d-p-p_none/huf.ipynb\n",
      "skip existing: f-color-d-c-d-p-p_none/f-mwpf-c-50-c-d.ipynb\n",
      "skip existing: f-color-d-c-d-p-p_none/bposd.ipynb\n",
      "skip existing: f-color-d-c-d-p-p_none/bposd-max-iter-5.ipynb\n",
      "skip existing: f-color-d-c-d-p-p_none/bposd-max-iter-5-osd-order-10-osd-method-osd-e.ipynb\n",
      "skip existing: f-color-d-c-d-p-p_none/bpuf.ipynb\n",
      "skip existing: f-color-d-c-d-p-p_none/bpuf-max-iter-5.ipynb\n",
      "skip existing: f-color-d-c-d-p-p_none/run_all.sh\n"
     ]
    }
   ],
   "source": [
    "d_vec = [3, 5, 7, 9, 11]\n",
    "\n",
    "code_parameters = dict(\n",
    "    code_eval = 'f\"color(d@{c.d},p@{p})\"',\n",
    "    noise_eval = '\"none\"',\n",
    "    p_center = 0.01,\n",
    "    per10_p_count = 3,\n",
    "    p_bias = -3,  # 0.001\n",
    "    slurm_mem_per_job = 4,\n",
    "    config_vec_eval=\"[\" + \",\".join(f\"dmap(d@{d})\" for d in d_vec) + \"]\",\n",
    "    slurm_maximum_jobs=30,  # smaller instances for kick starts\n",
    ")\n",
    "\n",
    "print(\"config_vec_eval:\", code_parameters[\"config_vec_eval\"])\n",
    "\n",
    "folder = slugify(code_parameters[\"code_eval\"]) + \"_\" + slugify(code_parameters[\"noise_eval\"])\n",
    "os.makedirs(folder, exist_ok=True)\n",
    "\n",
    "decoder_eval_vec = [\n",
    "    '\"mwpf\"',\n",
    "    '\"huf\"',\n",
    "    'f\"mwpf(c@{50*c.d})\"',  # auto scaling\n",
    "    '\"bposd\"',\n",
    "    '\"bposd(max_iter@5)\"',\n",
    "    '\"bposd(max_iter@5,osd_order@10,osd_method@osd_e)\"',\n",
    "    '\"bpuf\"',\n",
    "    '\"bpuf(max_iter@5)\"',\n",
    "]\n",
    "\n",
    "jobs: list[tuple[str, str]] = []\n",
    "for decoder_eval in decoder_eval_vec:\n",
    "    parameters = dict(\n",
    "        **code_parameters,\n",
    "        decoder_eval=decoder_eval,\n",
    "    )\n",
    "    notebook_filename = slugify(decoder_eval) + \".ipynb\"\n",
    "    notebook_path = os.path.join(folder, notebook_filename)\n",
    "    if os.path.exists(notebook_path):\n",
    "        print(\"skip existing:\", notebook_path)\n",
    "    else:\n",
    "        print(\"parameters:\", parameters)\n",
    "        pm.execute_notebook(\n",
    "            template_path,\n",
    "            notebook_path,\n",
    "            parameters=parameters,\n",
    "            prepare_only=True,  # do not run the notebook\n",
    "        )\n",
    "        remove_notebook_output(notebook_path)\n",
    "    # create tmux session to run these commands\n",
    "    tmux_name = slugify(decoder_eval) + \"_\" + slugify(code_parameters[\"code_eval\"]) + \"_\" + slugify(code_parameters[\"noise_eval\"])\n",
    "    command = f\"srun --time=23:55:00 --mem=10G --cpus-per-task=2 papermill {notebook_filename} ${{RID}}.{notebook_filename}\"\n",
    "    jobs.append((tmux_name, command))\n",
    "\n",
    "script_path = os.path.join(folder, \"run_all.sh\")\n",
    "script = tmux_list_script(jobs, prefix=\"RID=zz-3\")\n",
    "if os.path.exists(script_path):\n",
    "    print(\"skip existing:\", script_path)\n",
    "else:\n",
    "    with open(script_path, \"w\") as f:\n",
    "        f.write(script)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aebe2ce2",
   "metadata": {},
   "source": [
    "## BB Code (Code Capacity Noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "63ffe4d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "config_vec_eval: [dmap(n@72,k@12,d@6),dmap(n@90,k@8,d@10),dmap(n@108,k@8,d@10),dmap(n@144,k@12,d@12),dmap(n@288,k@12,d@18)]\n",
      "parameters: {'code_eval': 'f\"bb(n@{c.n},k@{c.k},d@{c.d})\"', 'noise_eval': 'f\"depolarize(p@{p})\"', 'p_center': 0.01, 'per10_p_count': 6, 'p_bias': 4, 'slurm_mem_per_job': 4, 'config_vec_eval': '[dmap(n@72,k@12,d@6),dmap(n@90,k@8,d@10),dmap(n@108,k@8,d@10),dmap(n@144,k@12,d@12),dmap(n@288,k@12,d@18)]', 'slurm_maximum_jobs': 30, 'decoder_eval': '\"mwpf\"'}\n",
      "parameters: {'code_eval': 'f\"bb(n@{c.n},k@{c.k},d@{c.d})\"', 'noise_eval': 'f\"depolarize(p@{p})\"', 'p_center': 0.01, 'per10_p_count': 6, 'p_bias': 4, 'slurm_mem_per_job': 4, 'config_vec_eval': '[dmap(n@72,k@12,d@6),dmap(n@90,k@8,d@10),dmap(n@108,k@8,d@10),dmap(n@144,k@12,d@12),dmap(n@288,k@12,d@18)]', 'slurm_maximum_jobs': 30, 'decoder_eval': '\"mwpf(c@1000)\"'}\n",
      "parameters: {'code_eval': 'f\"bb(n@{c.n},k@{c.k},d@{c.d})\"', 'noise_eval': 'f\"depolarize(p@{p})\"', 'p_center': 0.01, 'per10_p_count': 6, 'p_bias': 4, 'slurm_mem_per_job': 4, 'config_vec_eval': '[dmap(n@72,k@12,d@6),dmap(n@90,k@8,d@10),dmap(n@108,k@8,d@10),dmap(n@144,k@12,d@12),dmap(n@288,k@12,d@18)]', 'slurm_maximum_jobs': 30, 'decoder_eval': '\"huf\"'}\n",
      "parameters: {'code_eval': 'f\"bb(n@{c.n},k@{c.k},d@{c.d})\"', 'noise_eval': 'f\"depolarize(p@{p})\"', 'p_center': 0.01, 'per10_p_count': 6, 'p_bias': 4, 'slurm_mem_per_job': 4, 'config_vec_eval': '[dmap(n@72,k@12,d@6),dmap(n@90,k@8,d@10),dmap(n@108,k@8,d@10),dmap(n@144,k@12,d@12),dmap(n@288,k@12,d@18)]', 'slurm_maximum_jobs': 30, 'decoder_eval': 'f\"mwpf(c@{50*c.d})\"'}\n",
      "parameters: {'code_eval': 'f\"bb(n@{c.n},k@{c.k},d@{c.d})\"', 'noise_eval': 'f\"depolarize(p@{p})\"', 'p_center': 0.01, 'per10_p_count': 6, 'p_bias': 4, 'slurm_mem_per_job': 4, 'config_vec_eval': '[dmap(n@72,k@12,d@6),dmap(n@90,k@8,d@10),dmap(n@108,k@8,d@10),dmap(n@144,k@12,d@12),dmap(n@288,k@12,d@18)]', 'slurm_maximum_jobs': 30, 'decoder_eval': '\"bposd\"'}\n",
      "parameters: {'code_eval': 'f\"bb(n@{c.n},k@{c.k},d@{c.d})\"', 'noise_eval': 'f\"depolarize(p@{p})\"', 'p_center': 0.01, 'per10_p_count': 6, 'p_bias': 4, 'slurm_mem_per_job': 4, 'config_vec_eval': '[dmap(n@72,k@12,d@6),dmap(n@90,k@8,d@10),dmap(n@108,k@8,d@10),dmap(n@144,k@12,d@12),dmap(n@288,k@12,d@18)]', 'slurm_maximum_jobs': 30, 'decoder_eval': '\"bposd(max_iter@5)\"'}\n",
      "parameters: {'code_eval': 'f\"bb(n@{c.n},k@{c.k},d@{c.d})\"', 'noise_eval': 'f\"depolarize(p@{p})\"', 'p_center': 0.01, 'per10_p_count': 6, 'p_bias': 4, 'slurm_mem_per_job': 4, 'config_vec_eval': '[dmap(n@72,k@12,d@6),dmap(n@90,k@8,d@10),dmap(n@108,k@8,d@10),dmap(n@144,k@12,d@12),dmap(n@288,k@12,d@18)]', 'slurm_maximum_jobs': 30, 'decoder_eval': '\"bposd(max_iter@5,osd_order@10,osd_method@osd_e)\"'}\n",
      "parameters: {'code_eval': 'f\"bb(n@{c.n},k@{c.k},d@{c.d})\"', 'noise_eval': 'f\"depolarize(p@{p})\"', 'p_center': 0.01, 'per10_p_count': 6, 'p_bias': 4, 'slurm_mem_per_job': 4, 'config_vec_eval': '[dmap(n@72,k@12,d@6),dmap(n@90,k@8,d@10),dmap(n@108,k@8,d@10),dmap(n@144,k@12,d@12),dmap(n@288,k@12,d@18)]', 'slurm_maximum_jobs': 30, 'decoder_eval': '\"bpuf\"'}\n",
      "parameters: {'code_eval': 'f\"bb(n@{c.n},k@{c.k},d@{c.d})\"', 'noise_eval': 'f\"depolarize(p@{p})\"', 'p_center': 0.01, 'per10_p_count': 6, 'p_bias': 4, 'slurm_mem_per_job': 4, 'config_vec_eval': '[dmap(n@72,k@12,d@6),dmap(n@90,k@8,d@10),dmap(n@108,k@8,d@10),dmap(n@144,k@12,d@12),dmap(n@288,k@12,d@18)]', 'slurm_maximum_jobs': 30, 'decoder_eval': '\"bpuf(max_iter@5)\"'}\n"
     ]
    }
   ],
   "source": [
    "nkd_vec = [\n",
    "    (72, 12, 6),\n",
    "    (90, 8, 10),\n",
    "    (108, 8, 10),\n",
    "    (144, 12, 12),\n",
    "    (288, 12, 18)\n",
    "]\n",
    "\n",
    "code_parameters = dict(\n",
    "    code_eval = 'f\"bb(n@{c.n},k@{c.k},d@{c.d})\"',\n",
    "    noise_eval = 'f\"depolarize(p@{p})\"',\n",
    "    p_center = 0.01,\n",
    "    per10_p_count = 6,  # need more data for this code\n",
    "    p_bias = 4,  # 0.046\n",
    "    slurm_mem_per_job = 4,\n",
    "    config_vec_eval=\"[\" + \",\".join(f\"dmap(n@{n},k@{k},d@{d})\" for n, k, d in nkd_vec) + \"]\",\n",
    "    slurm_maximum_jobs=30,  # smaller instances for kick starts\n",
    ")\n",
    "\n",
    "print(\"config_vec_eval:\", code_parameters[\"config_vec_eval\"])\n",
    "\n",
    "folder = slugify(code_parameters[\"code_eval\"]) + \"_\" + slugify(code_parameters[\"noise_eval\"])\n",
    "os.makedirs(folder, exist_ok=True)\n",
    "\n",
    "decoder_eval_vec = [\n",
    "    '\"mwpf\"',\n",
    "    '\"mwpf(c@1000)\"',\n",
    "    '\"huf\"',\n",
    "    'f\"mwpf(c@{50*c.d})\"',  # auto scaling\n",
    "    '\"bposd\"',\n",
    "    '\"bposd(max_iter@5)\"',\n",
    "    '\"bposd(max_iter@5,osd_order@10,osd_method@osd_e)\"',\n",
    "    '\"bpuf\"',\n",
    "    '\"bpuf(max_iter@5)\"',\n",
    "]\n",
    "\n",
    "jobs: list[tuple[str, str]] = []\n",
    "for decoder_eval in decoder_eval_vec:\n",
    "    parameters = dict(\n",
    "        **code_parameters,\n",
    "        decoder_eval=decoder_eval,\n",
    "    )\n",
    "    notebook_filename = slugify(decoder_eval) + \".ipynb\"\n",
    "    notebook_path = os.path.join(folder, notebook_filename)\n",
    "    if os.path.exists(notebook_path):\n",
    "        print(\"skip existing:\", notebook_path)\n",
    "    else:\n",
    "        print(\"parameters:\", parameters)\n",
    "        pm.execute_notebook(\n",
    "            template_path,\n",
    "            notebook_path,\n",
    "            parameters=parameters,\n",
    "            prepare_only=True,  # do not run the notebook\n",
    "        )\n",
    "        remove_notebook_output(notebook_path)\n",
    "    # create tmux session to run these commands\n",
    "    tmux_name = slugify(decoder_eval) + \"_\" + slugify(code_parameters[\"code_eval\"]) + \"_\" + slugify(code_parameters[\"noise_eval\"])\n",
    "    command = f\"srun --time=23:55:00 --mem=10G --cpus-per-task=2 papermill {notebook_filename} ${{RID}}.{notebook_filename}\"\n",
    "    jobs.append((tmux_name, command))\n",
    "\n",
    "script_path = os.path.join(folder, \"run_all.sh\")\n",
    "script = tmux_list_script(jobs, prefix=\"RID=zz-3\")\n",
    "if os.path.exists(script_path):\n",
    "    print(\"skip existing:\", script_path)\n",
    "else:\n",
    "    with open(script_path, \"w\") as f:\n",
    "        f.write(script)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b963741",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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