sosaa-data/experiments/variables.ipynb

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   "source": [
    "import datetime\n",
    "import hashlib\n",
    "import itertools\n",
    "import json\n",
    "import os\n",
    "import pickle\n",
    "import re\n",
    "import warnings\n",
    "\n",
    "from collections import namedtuple\n",
    "from copy import copy\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "37913129",
   "metadata": {
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   "outputs": [],
   "source": [
    "import netCDF4\n",
    "from netCDF4 import Dataset\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8d898cee",
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   "source": [
    "TrajectoryPaths = namedtuple(\"TrajectoryPaths\", [\"date\", \"out\", \"aer\", \"ant\", \"bio\", \"met\"])\n",
    "TrajectoryDatasets = namedtuple(\"TrajectoryDatasets\", [\"date\", \"out\", \"aer\", \"ant\", \"bio\", \"met\"])\n",
    "MLDataset = namedtuple(\"MLDataset\", [\"date\", \"paths\", \"X_raw\", \"Y_raw\", \"X_train\", \"X_valid\", \"X_test\", \"Y_train\", \"Y_valid\", \"Y_test\", \"X_scaler\", \"Y_scaler\"])"
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "id": "c1c1fb4e",
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    "tags": []
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   "outputs": [],
   "source": [
    "OUTDIR_PATTERN = re.compile(r\"(\\d{4})(\\d{2})(\\d{2})_T(\\d{2})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "63c778cc",
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   "source": [
    "traj_datetimes = dict()\n",
    "\n",
    "base = Path.cwd().parent / \"trajectories\"\n",
    "\n",
    "for child in (base / \"outputs\" / \"baseline\").iterdir():\n",
    "    if not child.is_dir():\n",
    "        continue\n",
    "    \n",
    "    match = OUTDIR_PATTERN.match(child.name)\n",
    "    \n",
    "    if match is None:\n",
    "        continue\n",
    "        \n",
    "    date = datetime.datetime(\n",
    "        year=int(match.group(1)),\n",
    "        month=int(match.group(2)),\n",
    "        day=int(match.group(3)),\n",
    "        hour=int(match.group(4)),\n",
    "    )\n",
    "    \n",
    "    out_path = child / \"output.nc\"\n",
    "    aer_path = (\n",
    "        base / \"inputs\" / \"baseline\" / \"HYDE_BASE_Y2018\" /\n",
    "        f\"OUTPUT_bwd_{date.strftime('%Y%m%d')}\" /\n",
    "        \"EMISSIONS_0422\" /\n",
    "        f\"{date.strftime('%Y%m%d')}_7daybwd_Hyde_traj_AER_{24-date.hour:02}_L3.nc\"\n",
    "    )\n",
    "    ant_path = (\n",
    "        base / \"inputs\" / \"baseline\" / \"HYDE_BASE_Y2018\" /\n",
    "        f\"OUTPUT_bwd_{date.strftime('%Y%m%d')}\" /\n",
    "        \"EMISSIONS_0422\" /\n",
    "        f\"{date.strftime('%Y%m%d')}_7daybwd_Hyde_traj_ANT_{24-date.hour:02}_L3.nc\"\n",
    "    )\n",
    "    bio_path = (\n",
    "        base / \"inputs\" / \"baseline\" / \"HYDE_BASE_Y2018\" /\n",
    "        f\"OUTPUT_bwd_{date.strftime('%Y%m%d')}\" /\n",
    "        \"EMISSIONS_0422\" /\n",
    "        f\"{date.strftime('%Y%m%d')}_7daybwd_Hyde_traj_BIO_{24-date.hour:02}_L3.nc\"\n",
    "    )\n",
    "    met_path = (\n",
    "        base / \"inputs\" / \"baseline\" / \"HYDE_BASE_Y2018\" /\n",
    "        f\"OUTPUT_bwd_{date.strftime('%Y%m%d')}\" /\n",
    "        \"METEO\" /\n",
    "        f\"METEO_{date.strftime('%Y%m%d')}_R{24-date.hour:02}.nc\"\n",
    "    )\n",
    "    \n",
    "    if (\n",
    "        (not out_path.exists()) or (not aer_path.exists()) or\n",
    "        (not ant_path.exists()) or (not bio_path.exists()) or\n",
    "        (not met_path.exists())\n",
    "    ):\n",
    "        raise Exception(out_path, aer_path, ant_path, bio_path, met_path)\n",
    "    \n",
    "    traj_datetimes[date] = TrajectoryPaths(\n",
    "        date=date, out=out_path, aer=aer_path, ant=ant_path, bio=bio_path, met=met_path,\n",
    "    )\n",
    "\n",
    "traj_dates = sorted(set(d.date() for d in traj_datetimes.keys()))"
   ]
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   "source": [
    "def load_trajectory_dataset(paths: TrajectoryPaths) -> TrajectoryDatasets:\n",
    "    outds = Dataset(paths.out, \"r\", format=\"NETCDF4\")\n",
    "    aerds = Dataset(paths.aer, \"r\", format=\"NETCDF4\")\n",
    "    antds = Dataset(paths.ant, \"r\", format=\"NETCDF4\")\n",
    "    biods = Dataset(paths.bio, \"r\", format=\"NETCDF4\")\n",
    "    metds = Dataset(paths.met, \"r\", format=\"NETCDF4\")\n",
    "    \n",
    "    return TrajectoryDatasets(\n",
    "        date=paths.date, out=outds, aer=aerds, ant=antds, bio=biods, met=metds,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9aa7c6c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = datetime.datetime(year=2018, month=5, day=15, hour=19)\n",
    "ds = load_trajectory_dataset(traj_datetimes[dt])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c03a9c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time time Seconds since release at: 2018-05-15 19:00:00 indexed by time\n",
      "lev None None indexed by height level\n",
      "t Temperature K indexed by time and height level\n",
      "q Specific humidity kg kg**-1 indexed by time and height level\n",
      "ssr Surface net solar radiation W m**-2 indexed by time\n",
      "lsm Land-sea mask (0 - 1) indexed by time\n",
      "blh atmospheric_boundary_layer_height m indexed by time\n"
     ]
    }
   ],
   "source": [
    "for name, var in ds.met.variables.items():\n",
    "    if name not in [\"time\", \"lev\", \"t\", \"q\", \"ssr\", \"lsm\", \"blh\"]:\n",
    "        continue\n",
    "    \n",
    "    if var.shape == ds.met[\"time\"].shape:\n",
    "        dim = \"indexed by time\"\n",
    "    elif var.shape == ds.met[\"lev\"].shape:\n",
    "        dim = \"indexed by height level\"\n",
    "    elif var.shape == tuple(list(ds.met[\"time\"].shape) + list(ds.met[\"lev\"].shape)):\n",
    "        dim = \"indexed by time and height level\"\n",
    "    else:\n",
    "        raise Exception(\"unexpected shape\")\n",
    "    \n",
    "    print(name, getattr(var, \"long_name\", None), getattr(var, \"units\", None), dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "633620a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer Meters from ground indexed by height level\n",
      "time Seconds since release at: 2018-05-15 19:00:00 indexed by time\n",
      "3-10nm kg m-2 s-1 indexed by height level and time\n",
      "10-20nm kg m-2 s-1 indexed by height level and time\n",
      "20-30nm kg m-2 s-1 indexed by height level and time\n",
      "30-50nm kg m-2 s-1 indexed by height level and time\n",
      "50-70nm kg m-2 s-1 indexed by height level and time\n",
      "70-100nm kg m-2 s-1 indexed by height level and time\n",
      "100-200nm kg m-2 s-1 indexed by height level and time\n",
      "200-400nm kg m-2 s-1 indexed by height level and time\n",
      "400-1000nm kg m-2 s-1 indexed by height level and time\n"
     ]
    }
   ],
   "source": [
    "for name, var in ds.aer.variables.items():\n",
    "    if name in [\"lat\", \"lon\", \"bottom_layer_height\", \"mid_layer_height\", \"top_layer_height\", \"SRRsum\"]:\n",
    "        continue\n",
    "    \n",
    "    if var.shape == ds.aer[\"time\"].shape:\n",
    "        dim = \"indexed by time\"\n",
    "    elif var.shape == ds.aer[\"layer\"].shape:\n",
    "        dim = \"indexed by height level\"\n",
    "    elif var.shape == tuple(list(ds.aer[\"layer\"].shape) + list(ds.aer[\"time\"].shape)):\n",
    "        dim = \"indexed by height level and time\"\n",
    "    else:\n",
    "        raise Exception(\"unexpected shape\")\n",
    "    \n",
    "    print(name, getattr(var, \"units\", None), dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "da3424a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer Meters from ground indexed by height level\n",
      "time Seconds since release at: 2018-05-15 19:00:00 indexed by time\n",
      "co kg m-2 s-1 indexed by height level and time\n",
      "nox kg m-2 s-1 indexed by height level and time\n",
      "co2 kg m-2 s-1 indexed by height level and time\n",
      "nh3 kg m-2 s-1 indexed by height level and time\n",
      "ch4 kg m-2 s-1 indexed by height level and time\n",
      "so2 kg m-2 s-1 indexed by height level and time\n",
      "nmvoc kg m-2 s-1 indexed by height level and time\n",
      "alcohols kg m-2 s-1 indexed by height level and time\n",
      "ethane kg m-2 s-1 indexed by height level and time\n",
      "propane kg m-2 s-1 indexed by height level and time\n",
      "butanes kg m-2 s-1 indexed by height level and time\n",
      "pentanes kg m-2 s-1 indexed by height level and time\n",
      "hexanes kg m-2 s-1 indexed by height level and time\n",
      "ethene kg m-2 s-1 indexed by height level and time\n",
      "propene kg m-2 s-1 indexed by height level and time\n",
      "acetylene kg m-2 s-1 indexed by height level and time\n",
      "isoprene kg m-2 s-1 indexed by height level and time\n",
      "monoterpenes kg m-2 s-1 indexed by height level and time\n",
      "other-alkenes-and-alkynes kg m-2 s-1 indexed by height level and time\n",
      "benzene kg m-2 s-1 indexed by height level and time\n",
      "toluene kg m-2 s-1 indexed by height level and time\n",
      "xylene kg m-2 s-1 indexed by height level and time\n",
      "trimethylbenzene kg m-2 s-1 indexed by height level and time\n",
      "other-aromatics kg m-2 s-1 indexed by height level and time\n",
      "esters kg m-2 s-1 indexed by height level and time\n",
      "ethers kg m-2 s-1 indexed by height level and time\n",
      "formaldehyde kg m-2 s-1 indexed by height level and time\n",
      "other-aldehydes kg m-2 s-1 indexed by height level and time\n",
      "total-ketones kg m-2 s-1 indexed by height level and time\n",
      "total-acids kg m-2 s-1 indexed by height level and time\n",
      "other-VOCs kg m-2 s-1 indexed by height level and time\n"
     ]
    }
   ],
   "source": [
    "for name, var in ds.ant.variables.items():\n",
    "    if name in [\"lat\", \"lon\", \"bottom_layer_height\", \"mid_layer_height\", \"top_layer_height\", \"SRRsum\"]:\n",
    "        continue\n",
    "    \n",
    "    if var.shape == ds.ant[\"time\"].shape:\n",
    "        dim = \"indexed by time\"\n",
    "    elif var.shape == ds.ant[\"layer\"].shape:\n",
    "        dim = \"indexed by height level\"\n",
    "    elif var.shape == tuple(list(ds.ant[\"layer\"].shape) + list(ds.ant[\"time\"].shape)):\n",
    "        dim = \"indexed by height level and time\"\n",
    "    else:\n",
    "        raise Exception(\"unexpected shape\")\n",
    "    \n",
    "    print(name, getattr(var, \"units\", None), dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "203aa73c",
   "metadata": {},
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     "name": "stdout",
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     "text": [
      "time Seconds since release at: 2018-05-15 19:00:00 indexed by time\n",
      "acetaldehyde kg m-2 s-1 indexed by time\n",
      "acetone kg m-2 s-1 indexed by time\n",
      "butanes-and-higher-alkanes kg m-2 s-1 indexed by time\n",
      "butenes-and-higher-alkenes kg m-2 s-1 indexed by time\n",
      "CH4 kg m-2 s-1 indexed by time\n",
      "CO kg m-2 s-1 indexed by time\n",
      "ethane kg m-2 s-1 indexed by time\n",
      "ethanol kg m-2 s-1 indexed by time\n",
      "ethene kg m-2 s-1 indexed by time\n",
      "formaldehyde kg m-2 s-1 indexed by time\n",
      "hydrogen-cyanide kg m-2 s-1 indexed by time\n",
      "isoprene kg m-2 s-1 indexed by time\n",
      "MBO kg m-2 s-1 indexed by time\n",
      "methanol kg m-2 s-1 indexed by time\n",
      "methyl-bromide kg m-2 s-1 indexed by time\n",
      "methyl-chloride kg m-2 s-1 indexed by time\n",
      "methyl-iodide kg m-2 s-1 indexed by time\n",
      "other-aldehydes kg m-2 s-1 indexed by time\n",
      "other-ketones kg m-2 s-1 indexed by time\n",
      "other-monoterpenes kg m-2 s-1 indexed by time\n",
      "pinene-a kg m-2 s-1 indexed by time\n",
      "pinene-b kg m-2 s-1 indexed by time\n",
      "propane kg m-2 s-1 indexed by time\n",
      "propene kg m-2 s-1 indexed by time\n",
      "sesquiterpenes kg m-2 s-1 indexed by time\n",
      "toluene kg m-2 s-1 indexed by time\n",
      "CH2Br2 kg m-2 s-1 indexed by time\n",
      "CH3I kg m-2 s-1 indexed by time\n",
      "CHBr3 kg m-2 s-1 indexed by time\n",
      "DMS kg m-2 s-1 indexed by time\n"
     ]
    }
   ],
   "source": [
    "for name, var in ds.bio.variables.items():\n",
    "    if name in [\"lat\", \"lon\", \"bottom_layer_height\", \"mid_layer_height\", \"top_layer_height\", \"SRRsum\"]:\n",
    "        continue\n",
    "    \n",
    "    if var.shape == ds.bio[\"time\"].shape:\n",
    "        dim = \"indexed by time\"\n",
    "    elif var.shape == ds.bio[\"layer\"].shape:\n",
    "        dim = \"indexed by height level\"\n",
    "    elif var.shape == tuple(list(ds.bio[\"layer\"].shape) + list(ds.bio[\"time\"].shape)):\n",
    "        dim = \"indexed by height level and time\"\n",
    "    else:\n",
    "        raise Exception(\"unexpected shape\")\n",
    "    \n",
    "    print(name, getattr(var, \"units\", None), dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92c57cc5",
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "time time since the beginning of month s\n",
      "lev height above the ground m\n",
      "dp_dry_fs dry radius of aerosol particles in each size bin m\n",
      "nconc_par particle number concentration # m-3\n"
     ]
    }
   ],
   "source": [
    "for name, var in ds.out.variables.items():\n",
    "    if name not in [\"time\", \"lev\", \"dp_dry_fs\", \"nconc_par\"]:\n",
    "        continue\n",
    "    \n",
    "    print(name, getattr(var, \"long_name\", None), getattr(var, \"unit\", None))"
   ]
  },
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