experiment-3-large.ipynb

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    "# Experiment 3: Stress test\n",
    "As a test to ensure that the limits of are similar to METASPACE, this is one of the larger datasets that has been processed.\n",
    "\n",
    "### METRICS TO BENCHMARK\n",
    "* Performance:\n",
    "    * **Metric:** Total processing time\n",
    "\n",
    "        **Goal:** similar to or faster than METASPACE (including cluster start time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook setup\n",
    "Run `pip install -e .` in this directory to install all requirements for annotation pipeline project."
   ]
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   "source": [
    "# We need this to overcome Python notebooks limitations of too many open files\n",
    "import resource\n",
    "soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)\n",
    "print('Before:', soft, hard)\n",
    "\n",
    "# Raising the soft limit. Hard limits can be raised only by sudo users\n",
    "resource.setrlimit(resource.RLIMIT_NOFILE, (10000, hard))\n",
    "soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)\n",
    "print('After:', soft, hard)"
   ]
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   "cell_type": "code",
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   "source": [
    "%config Completer.use_jedi = False\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
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  {
   "cell_type": "code",
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   "metadata": {
    "pycharm": {
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   "source": [
    "import logging\n",
    "logging.basicConfig(level=logging.INFO)"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set a socket timeout so that CF requests fail instead of hanging if they don't get a response\n",
    "import socket\n",
    "print('Previous timeout:', socket.getdefaulttimeout())\n",
    "socket.setdefaulttimeout(60)"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Configuration"
   ]
  },
  {
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   "source": [
    "import json\n",
    "config = json.load(open('config.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# Uncomment one of the two sets of config files below:\n",
    "\n",
    "# Largest real-world dataset, typical database\n",
    "input_ds = json.load(open('metabolomics/ds_config6.json'))\n",
    "input_db = json.load(open('metabolomics/db_config2.json'))\n",
    "\n",
    "# Typical dataset, largest real-world database\n",
    "# input_ds = json.load(open('metabolomics/ds_config2.json'))\n",
    "# input_db = json.load(open('metabolomics/db_config5.json'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "metadata": false
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   },
   "source": [
    "# Benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "from annotation_pipeline.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline(config, input_ds, input_db, use_ds_cache=False, use_db_cache=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build molecular database and Run Annotation Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = datetime.now()\n",
    "\n",
    "# Run Annotation Pipeline:\n",
    "pipeline()\n",
    "\n",
    "finish_time = datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('start', start_time)\n",
    "print('finish', finish_time)\n",
    "print('processing time', finish_time - start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optionally get results (not part of the benchmark, but useful for debugging)\n",
    "results_df = pipeline.get_results()\n",
    "images_dict = pipeline.get_images()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display statistics file\n",
    "from annotation_pipeline.utils import PipelineStats\n",
    "PipelineStats.get()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean Temp Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline.clean()"
   ]
  }
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