materialize_branches.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4e578b0c-a7cf-429d-94e3-b0641970b2ca",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "awkward: 2.6.3\n",
      "dask-awkward: 2024.3.0\n",
      "uproot: 5.3.2\n",
      "hist: 2.7.2\n",
      "coffea: 2024.4.1\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import datetime\n",
    "import traceback\n",
    "\n",
    "import awkward as ak\n",
    "import dask\n",
    "import dask_awkward as dak\n",
    "import hist.dask\n",
    "import coffea\n",
    "import numpy as np\n",
    "import uproot\n",
    "from dask.distributed import Client, performance_report\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from coffea.nanoevents import NanoEventsFactory, NanoAODSchema\n",
    "from coffea.analysis_tools import PackedSelection\n",
    "from coffea import dataset_tools\n",
    "\n",
    "from functools import partial\n",
    "import os\n",
    "import time\n",
    "import warnings\n",
    "\n",
    "import utils  # worker count tracking\n",
    "\n",
    "executor = \"dask\"   # \"dask\" or \"taskvine\" or \"dask_gateway\"\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "NanoAODSchema.warn_missing_crossrefs = False # silences warnings about branches we will not use here\n",
    "\n",
    "    \n",
    "print(f\"awkward: {ak.__version__}\")\n",
    "print(f\"dask-awkward: {dak.__version__}\")\n",
    "print(f\"uproot: {uproot.__version__}\")\n",
    "print(f\"hist: {hist.__version__}\")\n",
    "print(f\"coffea: {coffea.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f891e515-6bef-4ac3-a72f-9fec2b13c25c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "scheduler_options = {}\n",
    "\n",
    "# for coffea-casa\n",
    "if executor == \"taskvine\":\n",
    "    from ndcctools.taskvine import DaskVine, Task\n",
    "    \n",
    "    \n",
    "    manager = DaskVine(port=8786, ssl=True, name=f\"{os.environ.get('USER', 'noname')}-coffea-casa\")\n",
    "\n",
    "    extra_files = {}\n",
    "    env_vars = {}\n",
    "    \n",
    "    token_acc_path = \"/etc/cmsaf-secrets-chown/access_token\"\n",
    "    token_xch_path = \"/etc/cmsaf-secrets-chown/xcache_token\"\n",
    "\n",
    "    if Path(token_acc_path).is_file():\n",
    "        extra_files[manager.declare_file(token_acc_path, cache=True)] = \"access_token\"\n",
    "        env_vars[\"BEARER_TOKEN_FILE\"] = \"access_token\"\n",
    "    if Path(token_xch_path).is_file():\n",
    "        extra_files[manager.declare_file(token_xch_path, cache=True)] = \"xcache_token\"\n",
    "        env_vars[\"XCACHE_FILE\"] = \"xcache_token\"\n",
    "\n",
    "    vine_scheduler = partial(manager.get,\n",
    "                             resources={\"cores\": 1, \"disk\": 2000},  #  max 1 core, 5GB of disk per task\n",
    "                             extra_files=extra_files,\n",
    "                             env_vars=env_vars,\n",
    "                             submit_per_cycle=1000,\n",
    "                             lazy_transfers=True,\n",
    "                             #  resources_mode=None,   # set to \"fixed\" to kill tasks on resources\n",
    "                            )\n",
    "    # change default scheduler\n",
    "    scheduler_options['scheduler'] = vine_scheduler\n",
    "elif executor == \"dask_gateway\":\n",
    "    num_workers = 400   #number of workers desired\n",
    "    from dask.distributed import LocalCluster, Client, progress\n",
    "    from dask_gateway import Gateway\n",
    "    import pathlib\n",
    "    \n",
    "    gateway = Gateway()\n",
    "    clusters=gateway.list_clusters()\n",
    "    cluster = gateway.connect(clusters[0].name)\n",
    "    client = cluster.get_client()\n",
    "    cluster.scale(num_workers)\n",
    "    # %%\n",
    "    def set_env(dask_worker):\n",
    "        path = str(pathlib.Path(dask_worker.local_directory) / 'access_token')\n",
    "        os.environ[\"BEARER_TOKEN_FILE\"] = path\n",
    "        os.chmod(path, 0o600)\n",
    "        os.chmod(\"/etc/grid-security/certificates\", 0o755)\n",
    "\n",
    "    client.wait_for_workers(num_workers)\n",
    "    client.upload_file(\"/etc/cmsaf-secrets/access_token\")\n",
    "    client.run(set_env)\n",
    "        \n",
    "else:\n",
    "    # by default use dask   \n",
    "    # local: single thread, single worker\n",
    "    from dask.distributed import LocalCluster, Client, progress\n",
    "    \n",
    "    # cluster = LocalCluster(n_workers=1, processes=False, threads_per_worker=1)\n",
    "    # client = Client(cluster)\n",
    "    client = Client(\"tls://localhost:8786\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "684f1240-a754-4dd1-b861-1bfac65ec288",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json\n",
    "fname = \"zstd_files.json\"\n",
    "fileset = {}\n",
    "with open(fname,'r') as fp:\n",
    "    for i,(dataset_name,file_list) in enumerate(json.load(fp).items()):\n",
    "        fileset[dataset_name] = {\"files\": {}}\n",
    "        for j,dataset_fpath in enumerate(file_list):\n",
    "            xrd_fpath = f\"root://xcache.cmsaf-dev.flatiron.hollandhpc.org:1094/{dataset_fpath}\"\n",
    "            fileset[dataset_name][\"files\"][xrd_fpath] = \"Events\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "05e87e21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of input files after filter: 63762\n"
     ]
    }
   ],
   "source": [
    "# apply optional filtering to limit number of input files\n",
    "\n",
    "# limite to the first N files per container, None if no limit\n",
    "LIMIT_NUM_FILES = None\n",
    "\n",
    "# limit to the first N containers, None if no limit\n",
    "LIMIT_NUM_CONTAINERS = None\n",
    "\n",
    "fileset = coffea.dataset_tools.max_files(fileset, LIMIT_NUM_FILES)\n",
    "\n",
    "if LIMIT_NUM_CONTAINERS is not None:\n",
    "    fileset = dict((k,v) for i, (k,v) in enumerate(fileset.items()) if i <LIMIT_NUM_CONTAINERS)\n",
    "\n",
    "print(f\"number of input files after filter: {sum([len(f['files']) for f in fileset.values()])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "633689aa-419d-4c79-a0b8-b36dad9abd79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# turn fileset into simple list of files to run over\n",
    "all_files = []\n",
    "for process in fileset:\n",
    "    all_files += fileset[process][\"files\"]\n",
    "\n",
    "# define work to be done\n",
    "def uproot_open_materialize(fname):\n",
    "    BRANCH_LIST = [\n",
    "        \"GenPart_pt\", \"GenPart_eta\", \"GenPart_phi\", \"CorrT1METJet_phi\",\n",
    "        \"GenJet_pt\", \"CorrT1METJet_eta\", \"SoftActivityJet_pt\",\n",
    "        \"Jet_eta\", \"Jet_phi\", \"SoftActivityJet_eta\", \"SoftActivityJet_phi\", \n",
    "        \"CorrT1METJet_rawPt\", \"Jet_btagDeepFlavB\", \"GenJet_eta\", \n",
    "        \"GenPart_mass\", \"GenJet_phi\",\n",
    "        \"Jet_puIdDisc\", \"CorrT1METJet_muonSubtrFactor\", \"Jet_btagDeepFlavCvL\",\n",
    "        \"Jet_btagDeepFlavQG\", \"Jet_mass\", \"Jet_pt\", \"GenPart_pdgId\",\n",
    "        \"Jet_btagDeepFlavCvB\", \"Jet_cRegCorr\"\n",
    "        ]\n",
    "\n",
    "    filter_name = lambda x: x in BRANCH_LIST\n",
    "\n",
    "    size_uncompressed = 0\n",
    "    t0 = time.perf_counter()\n",
    "    try:\n",
    "        with uproot.open(fname, filter_name=filter_name) as f:\n",
    "            num_entries = f[\"Events\"].num_entries\n",
    "            for b in BRANCH_LIST:\n",
    "                f[\"Events\"][b].array()\n",
    "                size_uncompressed += f[\"Events\"][b].uncompressed_bytes\n",
    "\n",
    "            size_read = f.file.source.num_requested_bytes\n",
    "        exception = None\n",
    "\n",
    "    except:\n",
    "        num_entries = 0\n",
    "        size_read = 0\n",
    "        size_uncompressed = 0\n",
    "        exception = traceback.format_exc()\n",
    "\n",
    "    t1 = time.perf_counter()\n",
    "    time_finished = datetime.datetime.now()\n",
    "    return {\"fname\": fname, \"read\": size_read, \"uncompressed\": size_uncompressed, \"num_entries\": num_entries,\n",
    "            \"runtime\": t1-t0, \"time_finished\": time_finished, \"exception\": exception}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "35833a73-9c4e-4b74-8029-8b138b0f8c35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with 63762 files\n",
      "wall clock time: 1464.08s\n",
      "current number of workers: 959\n",
      "sleeping for two seconds before reading out file to ensure tracking is done\n"
     ]
    }
   ],
   "source": [
    "# perform computation\n",
    "print(f\"running with {len(all_files)} files\")\n",
    "# scattered_data = client.scatter([f for f in all_files])  # instead of submitting (possibly big) object directly\n",
    "\n",
    "utils.worker_tracking.start_tracking_workers(client)  # track worker count in background\n",
    "t0 = time.perf_counter()\n",
    "with performance_report(filename=\"dask-report-plain-uproot.html\"):\n",
    "    # futures = client.map(uproot_open_materialize, scattered_data)\n",
    "    # out = ak.Array([r for r in client.gather(iter(futures))])\n",
    "    tasks = [dask.delayed(uproot_open_materialize)(f) for f in all_files]\n",
    "    out = ak.Array(dask.compute(*tasks))\n",
    "t1 = time.perf_counter()\n",
    "utils.worker_tracking.stop_tracking_workers()\n",
    "\n",
    "print(f\"wall clock time: {t1-t0:.2f}s\")\n",
    "print(f\"current number of workers: {len(client.scheduler_info()['workers'])}\")\n",
    "\n",
    "timestamps, nworkers, avg_num_workers = utils.worker_tracking.get_timestamps_and_counts()  # worker count info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "389b1ad5-5d6c-4845-8ab8-80bb3824156d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.style.use(\"ggplot\")\n",
    "\n",
    "# get arrays for starting time, runtime and end time of all tasks\n",
    "runtimes = np.asarray([datetime.timedelta(seconds=t) for t in out[\"runtime\"]], dtype=np.timedelta64)\n",
    "ends = out[\"time_finished\"].to_numpy()\n",
    "starts = ends - runtimes\n",
    "\n",
    "# calculate instantaneous rates for given timestamp\n",
    "times_for_rates = []\n",
    "instantaneous_rates = []\n",
    "for t in timestamps[::10]:  # only calculate every 10 seconds\n",
    "    mask = np.logical_and((starts <= t), (t <= ends))  # mask for tasks running at given timestamp\n",
    "    rate_Gbps_at_timestamp = sum(out[mask]['read']*8 / 1000**3 / out[mask][\"runtime\"])\n",
    "    # print(f\"instantaneous rate at t={t}: {rate_Gbps_at_timestamp:.2f} Gbps\")\n",
    "    times_for_rates.append(t)\n",
    "    instantaneous_rates.append(rate_Gbps_at_timestamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b6b37a13-496e-4325-8500-2c65bb08118a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total read (compressed): 29879.18 GB\n",
      "total read (uncompressed): 70850.02 GB\n",
      "average data rate: 163.26 Gbps (need to scale by x1 to reach 200 Gbps)\n",
      "total event rate (wall clock time): 27509.52 kHz (processed 40276255941 events total)\n",
      "total aggregated runtime in function: 1313392.16 s\n",
      "ratio total runtime / wall clock time: 897.07 (should match # cores without overhead / scheduling issues)\n",
      "time-averaged number of workers: 922.0\n",
      "\"efficiency\" (ratio of two numbers above): 97.3%\n",
      "event rate (aggregated time spent in function): 30.67 kHz\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summary of performance\n",
    "read_GB = sum(out['read']) / 1000**3\n",
    "print(f\"total read (compressed): {read_GB:.2f} GB\")\n",
    "print(f\"total read (uncompressed): {sum(out['uncompressed']) / 1000**3:.2f} GB\")\n",
    "\n",
    "rate_Gbps = read_GB*8/(t1-t0)\n",
    "print(f\"average data rate: {rate_Gbps:.2f} Gbps (need to scale by x{200/rate_Gbps:.0f} to reach 200 Gbps)\")\n",
    "\n",
    "n_evts = sum(out[\"num_entries\"])\n",
    "print(f\"total event rate (wall clock time): {n_evts / (t1-t0) / 1000:.2f} kHz (processed {n_evts} events total)\")\n",
    "\n",
    "total_runtime = sum(out[\"runtime\"])\n",
    "print(f\"total aggregated runtime in function: {total_runtime:.2f} s\")\n",
    "print(f\"ratio total runtime / wall clock time: {total_runtime / (t1-t0):.2f} \"\\\n",
    "      \"(should match # cores without overhead / scheduling issues)\")\n",
    "print(f\"time-averaged number of workers: {avg_num_workers:.1f}\")\n",
    "print(f\"\\\"efficiency\\\" (ratio of two numbers above): {total_runtime / (t1-t0) / avg_num_workers:.1%}\")\n",
    "print(f\"event rate (aggregated time spent in function): {n_evts / total_runtime / 1000:.2f} kHz\")\n",
    "\n",
    "utils.worker_tracking.plot_worker_count(timestamps, nworkers, avg_num_workers, times_for_rates, instantaneous_rates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "30523a52-060b-49eb-99cd-66a72b5cc795",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 files failed\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"{sum(o is not None for o in out['exception'])} files failed\\n\")\n",
    "\n",
    "# use below to get full list with details\n",
    "# for report in out:\n",
    "#     if report[\"exception\"] is not None:\n",
    "#         print(f\"{report['fname']} failed in {report['runtime']:.2f} s\\n{report['exception']}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a9e28acd-aeb3-45dc-a0cf-30dacddc9772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "bins = np.linspace(0, max(out[\"runtime\"])*1.01, 100)\n",
    "ax.hist(out[\"runtime\"], bins=bins)\n",
    "ax.set_xlabel(\"runtime [s]\")\n",
    "ax.set_xlim([0, ax.get_xlim()[1]])\n",
    "ax.set_ylabel(\"count\")\n",
    "ax.semilogy();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "57f7a4b5-3513-4d3a-aa6d-37506a48b380",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'runtime [s]')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(out[\"num_entries\"], out[\"runtime\"], marker=\"x\")\n",
    "ax.set_xlabel(\"number of events\")\n",
    "ax.set_ylabel(\"runtime [s]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2b0fe35-d324-4ec6-a710-f8a93901ac93",
   "metadata": {},
   "source": [
    "# below: coffea 2024 approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a38efe4-8024-422c-ba42-12bd3a3b44cd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def task(events):\n",
    "    # track number of events\n",
    "    num_events = ak.num(events, axis=0)\n",
    "\n",
    "    # read out all other branches into integers to avoid memory issues\n",
    "    _counter = 0\n",
    "    for obj_to_add in [\n",
    "        events.GenPart.pt,\n",
    "        events.GenPart.eta,\n",
    "        events.GenPart.phi,\n",
    "        events.CorrT1METJet.phi,\n",
    "        events.GenJet.pt, \n",
    "        events.CorrT1METJet.eta,\n",
    "        events.SoftActivityJet.pt,\n",
    "        events.Jet.eta,\n",
    "        events.Jet.phi,\n",
    "        events.SoftActivityJet.eta,\n",
    "        events.SoftActivityJet.phi, \n",
    "        events.CorrT1METJet.rawPt,\n",
    "        events.Jet.btagDeepFlavB,\n",
    "        events.GenJet.eta, \n",
    "        events.GenPart.mass,\n",
    "        events.GenJet.phi,\n",
    "        events.Jet.puIdDisc,\n",
    "        events.CorrT1METJet.muonSubtrFactor,\n",
    "        events.Jet.btagDeepFlavCvL,\n",
    "        events.Jet.btagDeepFlavQG,\n",
    "        events.Jet.mass,\n",
    "        events.Jet.pt,\n",
    "        events.GenPart.pdgId,\n",
    "        events.Jet.btagDeepFlavCvB,\n",
    "        events.Jet.cRegCorr\n",
    "        \n",
    "    ]:\n",
    "        _counter_to_add = ak.count_nonzero(obj_to_add, axis=1)\n",
    "\n",
    "        # reduce >2-dimensional (per event) branches further\n",
    "        for _ in range(_counter_to_add.ndim - 1):\n",
    "            _counter_to_add = ak.count_nonzero(_counter_to_add, axis=-1)\n",
    "\n",
    "        _counter = _counter + _counter_to_add  # sum 1-dim array built from new branch\n",
    "\n",
    "    _counter = ak.count_nonzero(_counter, axis=0)  # reduce to int\n",
    "\n",
    "    return {\"nevts\": num_events, \"_counter\": _counter}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed1ecc41-0a72-44ec-a8b8-654286a02196",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "# pre-process\n",
    "samples, report = dataset_tools.preprocess(fileset, skip_bad_files=True, uproot_options={\"allow_read_errors_with_report\": True}, **scheduler_options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d2544b5-603a-4aa7-b5db-03e6fcd4439c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# find issues where access did not work\n",
    "for process in report:\n",
    "    for k, v in report[process][\"files\"].items():\n",
    "        if v[\"steps\"] is None:\n",
    "            print(f\"could not read {k}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15835a57-1182-4efb-8306-07f36af7a5b2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "# create the task graph\n",
    "tasks = dataset_tools.apply_to_fileset(task, samples, uproot_options={\"allow_read_errors_with_report\": (OSError, TypeError, KeyError)})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1953d6d",
   "metadata": {},
   "source": [
    "execute task graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b6dfb17-6806-46c8-aa59-cd475e40cf64",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "# execute\n",
    "utils.worker_tracking.start_tracking_workers(client)  # track worker count in background\n",
    "t0 = time.perf_counter()\n",
    "\n",
    "if executor == \"taskvine\":\n",
    "    ((out, report),) = dask.compute(tasks, **scheduler_options)\n",
    "else:\n",
    "    with performance_report(filename=\"dask-report.html\"):\n",
    "        ((out, report),) = dask.compute(tasks, **scheduler_options)  # feels strange that this is a tuple-of-tuple\n",
    "t1 = time.perf_counter()\n",
    "utils.worker_tracking.stop_tracking_workers()\n",
    "\n",
    "time_uproot = ak.sum([v['duration'] for v in report.values()])\n",
    "print(f\"total time spent in uproot reading data: {time_uproot:.2f} s\")\n",
    "print(f\"wall time: {t1-t0:.2f}s\")\n",
    "\n",
    "timestamps, nworkers, avg_num_workers = utils.worker_tracking.get_timestamps_and_counts()  # worker count info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52c8c6a3-c938-4629-8ce1-6f50acce1b1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"output: {out}\")\n",
    "\n",
    "print(\"\\nperformance metrics:\")\n",
    "event_rate = sum([out[process][\"nevts\"] for process in out.keys()]) / (t1-t0)\n",
    "print(f\" - event rate: {event_rate / 1_000:.2f} kHz\")\n",
    "\n",
    "# need uproot>=5.3.2 to get these useful performance stats\n",
    "num_bytes = ak.sum([report[process][\"performance_counters\"][\"num_requested_bytes\"] for process in out.keys()])\n",
    "read_MB = num_bytes / 1_000**2\n",
    "rate_Mbs = read_MB / (t1-t0)\n",
    "print(f\" - read {read_MB:.2f} MB in {t1-t0:.2f} s -> {rate_Mbs*8:.2f} Mbps (need to scale by x{200/8/rate_Mbs*1000:.0f} to reach 200 Gbps)\")\n",
    "print(f\" - time-averaged number of workers: {avg_num_workers:.1f}\")\n",
    "print(f\" - spent {time_uproot:.1f} s reading data with wall time {t1-t0:.2f} and {avg_num_workers:.1f} cores on average -> \\\"efficiency\\\": {time_uproot / (t1-t0) / avg_num_workers:.1%}\")\n",
    "\n",
    "utils.worker_tracking.plot_worker_count(timestamps, nworkers, avg_num_workers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e749a960-df4c-4ada-856f-ef228783ceb9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# report problematic files that caused exceptions\n",
    "for process in report.keys():\n",
    "    for i_file in range(len(report[process].exception)):\n",
    "        file_report = report[process][i_file]\n",
    "        if file_report.exception is not None:\n",
    "            print(file_report.args[0].strip(\"\\'\"))\n",
    "            print(file_report.message + \"\\n\")"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "notebook_metadata_filter": "all,-jupytext.text_representation.jupytext_version"
  },
  "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}