Testing tomchors PR for xmovie

tc_xmovie_parallel_test.ipynb

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   "cell_type": "markdown",
   "id": "scenic-segment",
   "metadata": {},
   "source": [
    "# Testing Toms PR with the weird case..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "clear-broad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "from xmovie import Movie\n",
    "from xmovie.presets import rotating_globe_dark\n",
    "\n",
    "ds = xr.tutorial.open_dataset('air_temperature').isel(time=slice(0,150))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "inappropriate-school",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/juliusbusecke/code/xmovie/xmovie/core.py:51: UserWarning: No `vmin` provided. Data limits are calculated from input. Depending on the input this can take long. Pass `vmin` to avoid this step\n",
      "  UserWarning,\n",
      "/Users/juliusbusecke/code/xmovie/xmovie/core.py:58: UserWarning: No `vmax` provided. Data limits are calculated from input. Depending on the input this can take long. Pass `vmax` to avoid this step\n",
      "  UserWarning,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movie created at movie_rotating.mp4\n",
      "GIF created at movie_rotating.gif\n"
     ]
    }
   ],
   "source": [
    "from xmovie.presets import rotating_globe\n",
    "# specify custom plotfunctions (here a preset from xmovie)\n",
    "mov = Movie(ds.air, plotfunc=rotating_globe)\n",
    "\n",
    "# run one save without timing, because cartopy might cache the coastline data\n",
    "mov.save('movie_rotating.gif', progress=False, overwrite_existing=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "alike-stylus",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movie created at movie.mp4\n",
      "GIF created at movie.gif\n",
      "CPU times: user 39.4 s, sys: 528 ms, total: 40 s\n",
      "Wall time: 51.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "mov.save('movie.gif', progress=False, parallel=False, overwrite_existing=True, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "tracked-zoning",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movie created at movie_parallel.mp4\n",
      "GIF created at movie_parallel.gif\n",
      "CPU times: user 39.6 s, sys: 478 ms, total: 40.1 s\n",
      "Wall time: 51.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "mov.save('movie_parallel.gif',progress=False, parallel=True, overwrite_existing=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "resistant-microwave",
   "metadata": {},
   "source": [
    "This does not seem to speed things up?\n",
    "\n",
    "Can we improve with a Local Cluster?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "liquid-charlotte",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/juliusbusecke/miniconda/envs/test_env_xmovie/lib/python3.7/site-packages/distributed/node.py:155: UserWarning: Port 8787 is already in use.\n",
      "Perhaps you already have a cluster running?\n",
      "Hosting the HTTP server on port 53522 instead\n",
      "  http_address[\"port\"], self.http_server.port\n"
     ]
    }
   ],
   "source": [
    "from dask.distributed import Client, LocalCluster\n",
    "cluster = LocalCluster(n_workers=4)\n",
    "# explicitly connect to the cluster we just created\n",
    "client = Client(cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sitting-municipality",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)\n",
      "distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)\n",
      "distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)\n",
      "distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)\n",
      "distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)\n",
      "distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)\n",
      "distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)\n",
      "distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "mov.save('movie_parallel.gif',progress=False, parallel=True, overwrite_existing=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "insured-joshua",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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