clio_report.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inputs\n",
    "source_dir = \"BUCKET_NAME\" # source file bucket\n",
    "run_id = \"DAG_RUN_NAME\" # unique run id for job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from google.cloud import storage\n",
    "storage_client = storage.Client()\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from math import ceil\n",
    "import json\n",
    "\n",
    "# info locations\n",
    "process_dir = source_dir + \"_process\"\n",
    "prefix = run_id + \"/\"\n",
    "\n",
    "initloc = prefix + \"init.json\" # start file\n",
    "comploc = prefix + \"complete.json\" # final file\n",
    "\n",
    "align_dir = prefix + \"align/affine_cache/\" # affine cache\n",
    "slice_write_dir = prefix + \"align/write_cache/\" # write cache\n",
    "ng_write_dir = prefix + \"neuroglancer/cache/\" # write cache\n",
    "\n",
    "ng_dir =  source_dir  + \"_ng_\" + run_id  # location of ng bucket\n",
    "ng_meta = \"neuroglancer/jpeg/info\" # location of info\n",
    "\n",
    "neuroglancer_addr = f\"https://neuroglancer-demo.appspot.com/#!%7B%22layers%22%3A%5B%7B%22type%22%3A%22image%22%2C%22source%22%3A%7B%22url%22%3A%22precomputed%3A%2F%2Fgs%3A%2F%2F{ng_dir}%2Fneuroglancer%2Fjpeg%22%7D%2C%22tab%22%3A%22source%22%2C%22name%22%3A%22jpeg%22%7D%5D%2C%22selectedLayer%22%3A%7B%22layer%22%3A%22jpeg%22%2C%22visible%22%3Atrue%7D%7D\"\n",
    "\n",
    "def analyze_delays(prefix, total_tasks, hint=1):\n",
    "    \"\"\"Provides the following for a given stage:\n",
    "\n",
    "    1. Make bar chart with number of tasks complete out of remaining and report total time\n",
    "    2. Make graph of work per minute\n",
    "    3. Print estimated time remaining\n",
    "    \"\"\"\n",
    "    import warnings\n",
    "    import tqdm\n",
    "    \n",
    "    # collect all time stamps\n",
    "    blobs = storage_client.list_blobs(process_dir, prefix=prefix)\n",
    "    create_times = []\n",
    "    for blob in blobs:\n",
    "        if blob.name.startswith(prefix+\"worker\"):\n",
    "            continue\n",
    "        create_times.append(blob.time_created)\n",
    "\n",
    "    \n",
    "    # print work per minute rates\n",
    "    df = pd.DataFrame({\"time\": create_times})\n",
    "    df = df.sort_values(by=\"time\", ascending=True)\n",
    "    warnings.simplefilter(action='ignore', category=UserWarning)\n",
    "    \n",
    "        \n",
    "    # print bar chart and total time so far\n",
    "    remaining = total_tasks - len(create_times)\n",
    "    total_time = 0\n",
    "    \n",
    "    if len(create_times) > 0:\n",
    "        total_time = (df[\"time\"].iloc[-1]-df[\"time\"].iloc[0])\n",
    "        total_time = (total_time.total_seconds()/60)\n",
    "    \n",
    "    pbar = tqdm.tqdm(desc=f\"Tasks completed in {total_time:.2f} minutes\", bar_format=\"{l_bar}{bar}|{n_fmt}/{total_fmt}\", initial=len(create_times), total=total_tasks)\n",
    "    #pbar.set_description()\n",
    "    \n",
    "    if len(create_times) > 0:\n",
    "        counts = pd.Series(index=df.time, data=np.array(df.count)).resample('1T').count()\n",
    "        counts.sort_index(ascending=True)\n",
    "        counts.plot.line()\n",
    "    \n",
    "    # estimate work time based on last 15 minutes\n",
    "    remaining_time = 0\n",
    "    rate = hint\n",
    "    if remaining > 0 and len(create_times) > 0:\n",
    "        rate = counts.sort_index(ascending=False)[1:15].mean()\n",
    "        remaining_time = remaining / rate\n",
    "    elif len(create_times) > 0:\n",
    "        rate = counts.sort_index(ascending=False).mean()\n",
    "    elif remaining > 0:\n",
    "        remaining_time = remaining / rate\n",
    "\n",
    "    #print(f\"Total time {total_time:.2f} minutes\")\n",
    "    print(f\"Estimated time remaining: {remaining_time:.2f} minutes\")\n",
    "    print(f\"Task rate: {rate:.2f} per minute\")\n",
    "    return rate\n",
    "\n",
    "# get job data\n",
    "bucket = storage_client.bucket(process_dir)\n",
    "\n",
    "# determine number of slices\n",
    "blob = bucket.blob(initloc)\n",
    "job_json = json.loads(blob.download_as_string())\n",
    "\n",
    "num_slices = job_json[\"maxz\"] - job_json[\"minz\"] + 1\n",
    "\n",
    "shards = ceil(num_slices**3 / (1024**3)) # estimate\n",
    "\n",
    "try:\n",
    "    # estimate ng work if possible from ng file\n",
    "    bucket = storage_client.bucket(ng_dir)\n",
    "\n",
    "    # determine number of slices\n",
    "    blob = bucket.blob(ng_meta)\n",
    "    \n",
    "    ng_json = json.loads(blob.download_as_string())\n",
    "    xb = ceil(ng_json[\"scales\"][0][\"size\"][0] / 1024)\n",
    "    yb = ceil(ng_json[\"scales\"][0][\"size\"][1] / 1024)\n",
    "    zb = ceil(num_slices / 1024)\n",
    "    shards = xb*yb*zb\n",
    "except:\n",
    "    pass\n",
    "    \n",
    "# get runtime total\n",
    "import time\n",
    "\n",
    "total_runtime = 0\n",
    "job_status = \"not started\"\n",
    "\n",
    "start = 0\n",
    "end = time.time()\n",
    "\n",
    "blobs = storage_client.list_blobs(process_dir, prefix=initloc)\n",
    "\n",
    "for blob in blobs:\n",
    "    start = blob.time_created.timestamp()\n",
    "    job_status = \"started\"\n",
    "    \n",
    "if start != 0:\n",
    "    blobs = storage_client.list_blobs(process_dir, prefix=comploc)\n",
    "    for blob in blobs:\n",
    "        end = blob.time_created.timestamp()\n",
    "        job_status = \"completed\"\n",
    "    total_runtime = (end-start) / 60   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "status: completed\n",
      "total runtime: 107.86 minutes\n",
      "neuroglancer location: https://neuroglancer-demo.appspot.com/#!%7B%22layers%22%3A%5B%7B%22type%22%3A%22image%22%2C%22source%22%3A%7B%22url%22%3A%22precomputed%3A%2F%2Fgs%3A%2F%2Fclio_medulla_38_ng_ingest_9a05245f240e%2Fneuroglancer%2Fjpeg%22%7D%2C%22tab%22%3A%22source%22%2C%22name%22%3A%22jpeg%22%7D%5D%2C%22selectedLayer%22%3A%7B%22layer%22%3A%22jpeg%22%2C%22visible%22%3Atrue%7D%7D\n"
     ]
    }
   ],
   "source": [
    "print(f\"status: {job_status}\")\n",
    "print(f\"total runtime: {total_runtime:.2f} minutes\")\n",
    "if job_status == \"completed\":\n",
    "    print(f\"neuroglancer location: {neuroglancer_addr}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alignment Stage\n",
    "\n",
    "Affine transformation calculation for each image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Tasks completed in 40.65 minutes: 100%|██████████|13695/13695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated time remaining: 0.00 minutes\n",
      "Task rate: 334.02 per minute\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# alignment stage\n",
    "align_rate = analyze_delays(align_dir, num_slices-1, hint=256)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Slice Transform and Contrast Adjust Stage\n",
    "\n",
    "Apply alignment and write out intermediate tiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Tasks completed in 39.20 minutes: 100%|██████████|13696/13696\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated time remaining: 0.00 minutes\n",
      "Task rate: 342.40 per minute\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Tasks completed in 39.20 minutes: 100%|██████████|13696/13696\n"
     ]
    }
   ],
   "source": [
    "# slice write stage\n",
    "slice_rate = analyze_delays(slice_write_dir, num_slices, hint=align_rate/4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export neuroglancer and chunks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Tasks completed in 19.28 minutes: 100%|██████████|784/784\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated time remaining: 0.00 minutes\n",
      "Task rate: 39.20 per minute\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# slice write stage\n",
    "slice_rate = analyze_delays(ng_write_dir, shards, hint=256/3)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}