camels_spat_exploring_forcings.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import os\n",
    "import sys\n",
    "from pathlib import Path\n",
    "\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import yaml\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from camels_spat2nh import *\n",
    "\n",
    "# Get the current working directory of the notebook\n",
    "CURRENT_DIR = os.getcwd()\n",
    "# Add the parent directory of the notebook to the Python path\n",
    "# root_dir = os.path.abspath(os.path.join(current_dir, '..', '..'))\n",
    "ROOT_DIR = os.path.abspath(CURRENT_DIR)\n",
    "sys.path.append(ROOT_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xarray version: 2023.1.0\n",
      "pandas version: 2.0.3\n",
      "numpy version: 1.25.2\n",
      "yaml version: 6.0.1\n"
     ]
    }
   ],
   "source": [
    "# Print packages version\n",
    "print(f'xarray version: {xr.__version__}')\n",
    "print(f'pandas version: {pd.__version__}')\n",
    "print(f'numpy version: {np.__version__}')\n",
    "print(f'yaml version: {yaml.__version__}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "basin_id = '14400000'\n",
    "country = 'USA'\n",
    "\n",
    "basin_folder = f'{country}_{basin_id}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Util data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir_dict, data_gen_dict = load_util_data(CURRENT_DIR)\n",
    "# display(\n",
    "#     data_dir_dict,\n",
    "#     data_gen_dict\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "cspat_data_path = Path(data_dir_dict['data_dir_camels_spat']) / 'basin_data'\n",
    "relative_path_forcing = data_dir_dict['relative_path_forcing']\n",
    "countries = data_gen_dict['countries']\n",
    "data_sources = data_gen_dict['data_sources']\n",
    "\n",
    "# display(\n",
    "#     cspat_data_path,\n",
    "#     countries\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find basin and list of forcing files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USA_14400000 folder found in /project/gwf/gwf_cmt/wknoben/camels_spat/camels-spat-data/basin_data\n"
     ]
    }
   ],
   "source": [
    "# List all the folders in the basin_data directory\n",
    "basin_data_folders = [str(f).split('/')[-1] for f in cspat_data_path.iterdir() if f.is_dir()]\n",
    "# Check if basin_folder exists in the basin_data directory\n",
    "if basin_folder not in basin_data_folders:\n",
    "    raise FileNotFoundError(f'{basin_folder} folder not found in {cspat_data_path}')\n",
    "else:\n",
    "    print(f'{basin_folder} folder found in {cspat_data_path}')\n",
    "    basin_folder_path = cspat_data_path / basin_folder / relative_path_forcing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of files in ERA5: 640\n",
      "Number of files in EM_Earth: 603\n",
      "Number of files in RDRS: 468\n",
      "Number of files in daymet: 44\n"
     ]
    }
   ],
   "source": [
    "# Group the files in the basin_folder by the data_source\n",
    "# if contains data_source in the name\n",
    "basin_files = {}\n",
    "for data_source in data_sources:\n",
    "    basin_files[data_source] = sorted([f for f in basin_folder_path.iterdir() if data_source in str(f)])\n",
    "\n",
    "# Print number of files in each data_source\n",
    "for data_source in data_sources:\n",
    "    print(f'Number of files in {data_source}: {len(basin_files[data_source])}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the first file for each data_source\n",
    "basin_data = {}\n",
    "for data_source in data_sources:\n",
    "    basin_data[data_source] = xr.open_dataset(basin_files[data_source][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Display the data variables in the basin_data\n",
    "# for data_source in data_sources:\n",
    "#     display(data_source, basin_data[data_source])\n",
    "#     # List of Data Variables\n",
    "#     print(f'Data Variables in {data_source}: {list(basin_data[data_source].data_vars)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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       "  * time       (time) datetime64[ns] 1980-01-01T12:00:00 ... 1980-12-31T12:00:00\n",
       "Dimensions without coordinates: hru, nv\n",
       "Data variables:\n",
       "    latitude   (hru) float64 ...\n",
       "    longitude  (hru) float64 ...\n",
       "    hruId      (hru) float64 ...\n",
       "    prcp       (time, hru) float32 ...\n",
       "    tmax       (time, hru) float32 ...\n",
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       "Attributes:\n",
       "    Conventions:    CF-1.6\n",
       "    Author:         The data were written by CAMELS-spat workflow\n",
       "    License:        GPLv3+; Originla license: No license from original file i...\n",
       "    History:        Created Wed Jun 19 22:49:50 2024by EASYMORE nc_remapper; ...\n",
       "    easymore_hash:  835a10df80cccc9ceaef12a22497986cfa7f87a6f15cba6e6a34f7276...\n",
       "    Source:         Remapped by EASYMORE nc_remapper from original file: /gpf...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-eb9fc9df-04b9-452f-9ef6-0f824c4f5459' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eb9fc9df-04b9-452f-9ef6-0f824c4f5459' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 366</li><li><span>hru</span>: 1</li><li><span>nv</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2c396102-2544-4532-a6c9-a584d6368686' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2c396102-2544-4532-a6c9-a584d6368686' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01T12:00:00 ... 1980-12-...</div><input id='attrs-b0489340-3429-4297-b3bb-77fd38e49eb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0489340-3429-4297-b3bb-77fd38e49eb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ecb300a-5562-471b-ba06-d9d34453ac2d' class='xr-var-data-in' type='checkbox'><label for='data-2ecb300a-5562-471b-ba06-d9d34453ac2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>24-hour day based on local time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T12:00:00.000000000&#x27;, &#x27;1980-01-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;1980-01-03T12:00:00.000000000&#x27;, ..., &#x27;1980-12-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;1980-12-30T12:00:00.000000000&#x27;, &#x27;1980-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-29a56d25-b68f-4852-a42d-85e8ba56d6f4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-29a56d25-b68f-4852-a42d-85e8ba56d6f4' class='xr-section-summary' >Data variables: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(hru)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-66bcdf6e-6248-493e-bd7d-aacb645d4061' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66bcdf6e-6248-493e-bd7d-aacb645d4061' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b23c4159-dba3-4566-9296-d985f4b0a6d4' class='xr-var-data-in' type='checkbox'><label for='data-b23c4159-dba3-4566-9296-d985f4b0a6d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(hru)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-94393939-8c87-4da4-bd08-1ff582e33301' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94393939-8c87-4da4-bd08-1ff582e33301' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd6d6334-fe37-45c9-bcec-2a92fc96fed7' class='xr-var-data-in' type='checkbox'><label for='data-cd6d6334-fe37-45c9-bcec-2a92fc96fed7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hruId</span></div><div class='xr-var-dims'>(hru)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5643a264-1b33-4882-9409-4eccc4b1e0a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5643a264-1b33-4882-9409-4eccc4b1e0a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5db3129e-7b39-4974-affe-746ca3e8d78b' class='xr-var-data-in' type='checkbox'><label for='data-5db3129e-7b39-4974-affe-746ca3e8d78b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>shape ID</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prcp</span></div><div class='xr-var-dims'>(time, hru)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-66ba6584-a3a0-4da6-a5f2-14c6899121ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66ba6584-a3a0-4da6-a5f2-14c6899121ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97cc955c-ea60-404e-9224-4b171b48b11c' class='xr-var-data-in' type='checkbox'><label for='data-97cc955c-ea60-404e-9224-4b171b48b11c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>daily total precipitation</dd><dt><span>units :</span></dt><dd>mm/day</dd></dl></div><div class='xr-var-data'><pre>[366 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tmax</span></div><div class='xr-var-dims'>(time, hru)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7317c9ac-8ff8-4d94-9d80-321d4eac7ac5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7317c9ac-8ff8-4d94-9d80-321d4eac7ac5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69d8003a-1730-463f-9f68-20182ac334ab' class='xr-var-data-in' type='checkbox'><label for='data-69d8003a-1730-463f-9f68-20182ac334ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>daily maximum temperature</dd><dt><span>units :</span></dt><dd>degrees C</dd></dl></div><div class='xr-var-data'><pre>[366 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tmin</span></div><div class='xr-var-dims'>(time, hru)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b92d139a-3902-4160-a56e-c713e03a9b45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b92d139a-3902-4160-a56e-c713e03a9b45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-848b1e4b-6e3e-4345-9c76-b55075ade590' class='xr-var-data-in' type='checkbox'><label for='data-848b1e4b-6e3e-4345-9c76-b55075ade590' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>daily minimum temperature</dd><dt><span>units :</span></dt><dd>degrees C</dd></dl></div><div class='xr-var-data'><pre>[366 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>srad</span></div><div class='xr-var-dims'>(time, hru)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-50590d69-6753-4535-8d12-d9bbc25cabfb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50590d69-6753-4535-8d12-d9bbc25cabfb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff21b821-fed3-42bb-a1fa-a1554873ff8a' class='xr-var-data-in' type='checkbox'><label for='data-ff21b821-fed3-42bb-a1fa-a1554873ff8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>daylight average incident shortwave radiation</dd><dt><span>units :</span></dt><dd>W/m2</dd></dl></div><div class='xr-var-data'><pre>[366 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vp</span></div><div class='xr-var-dims'>(time, hru)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7a2a45b5-4ef7-4409-8041-8ad53549b45e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a2a45b5-4ef7-4409-8041-8ad53549b45e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5db609c4-2e1f-4cab-90f1-34a40d37655b' class='xr-var-data-in' type='checkbox'><label for='data-5db609c4-2e1f-4cab-90f1-34a40d37655b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>daily average vapor pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><pre>[366 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dayl</span></div><div class='xr-var-dims'>(time, hru)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bb12ec66-2a84-4af9-95f4-4541c13979aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb12ec66-2a84-4af9-95f4-4541c13979aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-617dacd5-a3df-4a75-845f-ad711bfee2d0' class='xr-var-data-in' type='checkbox'><label for='data-617dacd5-a3df-4a75-845f-ad711bfee2d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>daylength</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><pre>[366 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pet</span></div><div class='xr-var-dims'>(time, hru)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c62b7418-9533-4450-9b98-ee4c12d7fb6e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c62b7418-9533-4450-9b98-ee4c12d7fb6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ef74e52-7203-449d-af35-a510abfc6d06' class='xr-var-data-in' type='checkbox'><label for='data-9ef74e52-7203-449d-af35-a510abfc6d06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>potential evapotranspiration estimated with Priestley-Taylor method</dd><dt><span>units :</span></dt><dd>mm/day</dd></dl></div><div class='xr-var-data'><pre>[366 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nv)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-98f0022d-bd1e-439d-a815-6e89741d775f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98f0022d-bd1e-439d-a815-6e89741d775f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aba76d6e-1e12-4f2c-b5ac-e83f56a7595b' class='xr-var-data-in' type='checkbox'><label for='data-aba76d6e-1e12-4f2c-b5ac-e83f56a7595b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[732 values with dtype=datetime64[ns]]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-24e31fc6-f73e-4985-b0fb-9feb20007801' class='xr-section-summary-in' type='checkbox'  ><label for='section-24e31fc6-f73e-4985-b0fb-9feb20007801' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cf872532-2ac9-4bbb-b399-9ad15c47b8f7' class='xr-index-data-in' type='checkbox'/><label for='index-cf872532-2ac9-4bbb-b399-9ad15c47b8f7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1980-01-01 12:00:00&#x27;, &#x27;1980-01-02 12:00:00&#x27;,\n",
       "               &#x27;1980-01-03 12:00:00&#x27;, &#x27;1980-01-04 12:00:00&#x27;,\n",
       "               &#x27;1980-01-05 12:00:00&#x27;, &#x27;1980-01-06 12:00:00&#x27;,\n",
       "               &#x27;1980-01-07 12:00:00&#x27;, &#x27;1980-01-08 12:00:00&#x27;,\n",
       "               &#x27;1980-01-09 12:00:00&#x27;, &#x27;1980-01-10 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;1980-12-22 12:00:00&#x27;, &#x27;1980-12-23 12:00:00&#x27;,\n",
       "               &#x27;1980-12-24 12:00:00&#x27;, &#x27;1980-12-25 12:00:00&#x27;,\n",
       "               &#x27;1980-12-26 12:00:00&#x27;, &#x27;1980-12-27 12:00:00&#x27;,\n",
       "               &#x27;1980-12-28 12:00:00&#x27;, &#x27;1980-12-29 12:00:00&#x27;,\n",
       "               &#x27;1980-12-30 12:00:00&#x27;, &#x27;1980-12-31 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=366, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2aec6245-6435-4127-901c-e533b8033c57' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2aec6245-6435-4127-901c-e533b8033c57' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>Author :</span></dt><dd>The data were written by CAMELS-spat workflow</dd><dt><span>License :</span></dt><dd>GPLv3+; Originla license: No license from original file is detected</dd><dt><span>History :</span></dt><dd>Created Wed Jun 19 22:49:50 2024by EASYMORE nc_remapper; original file history:  history: Tue Jun 18 22:43:46 2024: ncks -O -d y,4721,4763 -d x,2672,2719 /gpfs/tp/gwf/gwf_cmt/wknoben/camels_spat/temp/daymet/daymet_v4_daily_na_dayl_1980.nc /gpfs/tp/gwf/gwf_cmt/wknoben/camels_spat/camels-spat-data/basin_data/USA_14400000/forcing/raw/daymet_year/USA_14400000_subset_daymet_v4_daily_na_dayl_1980.nc</dd><dt><span>easymore_hash :</span></dt><dd>835a10df80cccc9ceaef12a22497986cfa7f87a6f15cba6e6a34f72763b24f6f</dd><dt><span>Source :</span></dt><dd>Remapped by EASYMORE nc_remapper from original file: /gpfs/tp/gwf/gwf_cmt/wknoben/camels_spat/camels-spat-data/basin_data/USA_14400000/forcing/raw/daymet_1980.nc</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 366, hru: 1, nv: 2)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1980-01-01T12:00:00 ... 1980-12-31T12:00:00\n",
       "Dimensions without coordinates: hru, nv\n",
       "Data variables:\n",
       "    latitude   (hru) float64 ...\n",
       "    longitude  (hru) float64 ...\n",
       "    hruId      (hru) float64 ...\n",
       "    prcp       (time, hru) float32 ...\n",
       "    tmax       (time, hru) float32 ...\n",
       "    tmin       (time, hru) float32 ...\n",
       "    srad       (time, hru) float32 ...\n",
       "    vp         (time, hru) float32 ...\n",
       "    dayl       (time, hru) float32 ...\n",
       "    pet        (time, hru) float32 ...\n",
       "    time_bnds  (time, nv) datetime64[ns] ...\n",
       "Attributes:\n",
       "    Conventions:    CF-1.6\n",
       "    Author:         The data were written by CAMELS-spat workflow\n",
       "    License:        GPLv3+; Originla license: No license from original file i...\n",
       "    History:        Created Wed Jun 19 22:49:50 2024by EASYMORE nc_remapper; ...\n",
       "    easymore_hash:  835a10df80cccc9ceaef12a22497986cfa7f87a6f15cba6e6a34f7276...\n",
       "    Source:         Remapped by EASYMORE nc_remapper from original file: /gpf..."
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basin_data['daymet']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1555239b7dd0>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "basin_data['daymet'].pet.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv-camelsspat",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}