notebooks/models/run-pyCIAM-diaz2016.ipynb

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   "source": [
    "# Run pyCIAM in a Diaz 2016 configuration, using Diaz 2016 Inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "sys.path.append(\"../\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import distributed as dd\n",
    "import pandas as pd\n",
    "from pyCIAM.run import execute_pyciam\n",
    "from shared import (\n",
    "    AUTHOR,\n",
    "    CONTACT,\n",
    "    DIR_SCRATCH,\n",
    "    HISTORY,\n",
    "    PATH_DIAZ_INPUTS_INT,\n",
    "    PATH_DIAZ_RES,\n",
    "    PATH_PARAMS_DIAZ,\n",
    "    STORAGE_OPTIONS,\n",
    "    save,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# number of seg to run at once\n",
    "SEG_CHUNKSIZE = 4050\n",
    "\n",
    "# parameter object\n",
    "PARAMS = pd.read_json(PATH_PARAMS_DIAZ)[\"values\"]\n",
    "\n",
    "# attrs\n",
    "DESCRIPTION = (\n",
    "    \"Projected coastal damages from the Coastal Impact and Adaptation Model (CIAM, \"\n",
    "    \"Diaz 2016), using original Diaz 2016 inputs and assumptions.\"\n",
    ")\n",
    "\n",
    "HISTORY = (\n",
    "    \"\"\"Replication of Diaz 2016.\n",
    "\n",
    "\"\"\"\n",
    "    + HISTORY\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "<Client: 'tcp://127.0.0.1:43269' processes=3 threads=3, memory=45.00 GiB>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Small enough to run locally so we just use a local cluster by default here\n",
    "client = dd.Client(threads_per_worker=1, n_workers=3)\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "out = execute_pyciam(\n",
    "    PATH_PARAMS_DIAZ,\n",
    "    PATH_DIAZ_INPUTS_INT,\n",
    "    [None],\n",
    "    [None],\n",
    "    DIR_SCRATCH / \"refA-diaz.zarr\",\n",
    "    seg_var=\"seg\",\n",
    "    quantiles=None,\n",
    "    extra_attrs={\n",
    "        \"author\": AUTHOR,\n",
    "        \"history\": HISTORY,\n",
    "        \"contact\": CONTACT,\n",
    "        \"description\": DESCRIPTION,\n",
    "    },\n",
    "    refA_seg_chunksize=SEG_CHUNKSIZE,\n",
    "    pyciam_seg_chunksize=SEG_CHUNKSIZE,\n",
    "    diaz_inputs=True,\n",
    "    diaz_config=True,\n",
    "    dask_client_func=lambda: client,\n",
    "    storage_options=STORAGE_OPTIONS,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k, v in out.coords.items():\n",
    "    if v.dtype == object:\n",
    "        out[k] = v.astype(\"unicode\")\n",
    "save(out, PATH_DIAZ_RES, mode=\"w\")"
   ]
  }
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