From c15a8df3b930053218e387eeb14b21305a7ac59e Mon Sep 17 00:00:00 2001
From: "Richard R. McDonald" <rmcd@usgs.gov>
Date: Fri, 29 May 2020 18:33:31 -0600
Subject: [PATCH 1/3] fixed speed

---
 Examples/Deleware_example.ipynb | 1848 +++++++++++++++++++++++++++++++
 gridmetetl/etl.py               |  224 ++--
 gridmetetl/gridmet_etl.py       |   20 +-
 3 files changed, 1969 insertions(+), 123 deletions(-)
 create mode 100644 Examples/Deleware_example.ipynb

diff --git a/Examples/Deleware_example.ipynb b/Examples/Deleware_example.ipynb
new file mode 100644
index 0000000..4d37902
--- /dev/null
+++ b/Examples/Deleware_example.ipynb
@@ -0,0 +1,1848 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import geopandas as gpd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import datetime as dt\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the path B:\\gitbmi\\DelData\\output exits:  True\n",
+      "the path B:\\gitbmi\\DelData exits:  True\n",
+      "the path B:\\gitbmi\\DelData exits:  True\n",
+      "the file B:\\gitbmi\\DelData\\Delaware_weights_hru_v1_0.csv exits:  True\n"
+     ]
+    }
+   ],
+   "source": [
+    "p_gm = Path('B:\\gitbmi\\DelData\\output')\n",
+    "p_gmr = Path('B:\\gitbmi\\DelData')\n",
+    "#I put the gf_v11 shapefile, only hru polys, in the following folder for processing so we'll use it for plotting\n",
+    "shpf_p = Path('B:\\gitbmi\\DelData')\n",
+    "# weights file used to map gridded gridmet to hrus is here\n",
+    "gm_wght_f = Path('B:\\gitbmi\\DelData') / 'Delaware_weights_hru_v1_0.csv'\n",
+    "print(f'the path {p_gm} exits: ', p_gm.exists())\n",
+    "print(f'the path {p_gmr} exits: ', p_gmr.exists())\n",
+    "print(f'the path {shpf_p} exits: ', shpf_p.exists())\n",
+    "print(f'the file {gm_wght_f} exits: ', gm_wght_f.exists())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 LAYER region  hru_id_nat  nhm_id  model_idx  \\\n",
+      "0    Unknown Area Type     02        5308    5308        173   \n",
+      "1    Unknown Area Type     02        5309    5309        175   \n",
+      "2    Unknown Area Type     02        5310    5310        174   \n",
+      "3    Unknown Area Type     02        5311    5311        176   \n",
+      "4    Unknown Area Type     02        5312    5312        177   \n",
+      "..                 ...    ...         ...     ...        ...   \n",
+      "760  Unknown Area Type     02        7248    7248        209   \n",
+      "761  Unknown Area Type     02        7249    7249        184   \n",
+      "762  Unknown Area Type     02        7250    7250        216   \n",
+      "763  Unknown Area Type     02        7251    7251        221   \n",
+      "764  Unknown Area Type     02        7252    7252        448   \n",
+      "\n",
+      "                                              geometry  \n",
+      "0    MULTIPOLYGON (((-75.13599 38.75503, -75.13564 ...  \n",
+      "1    POLYGON ((-75.21891 38.82734, -75.21861 38.827...  \n",
+      "2    POLYGON ((-75.16132 38.78953, -75.16142 38.789...  \n",
+      "3    MULTIPOLYGON (((-75.25613 38.73334, -75.25579 ...  \n",
+      "4    POLYGON ((-75.22050 38.83309, -75.22084 38.833...  \n",
+      "..                                                 ...  \n",
+      "760  POLYGON ((-74.59461 42.04596, -74.59432 42.045...  \n",
+      "761  POLYGON ((-74.49984 42.24245, -74.49992 42.242...  \n",
+      "762  POLYGON ((-74.59760 41.92733, -74.59795 41.927...  \n",
+      "763  MULTIPOLYGON (((-74.76498 41.88257, -74.76534 ...  \n",
+      "764  POLYGON ((-74.46415 42.02282, -74.46423 42.022...  \n",
+      "\n",
+      "[765 rows x 6 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "shapefiles = shpf_p.glob(\"*.shp\")\n",
+    "gdf = pd.concat([\n",
+    "    gpd.read_file(shp)\n",
+    "    for shp in shapefiles\n",
+    "]).pipe(gpd.GeoDataFrame)\n",
+    "gdf.reset_index(drop=True, inplace=True)\n",
+    "# gdf.plot()\n",
+    "print(gdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wght_id: hru_id_nat\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>grid_ids</th>\n",
+       "      <th>hru_id_nat</th>\n",
+       "      <th>w</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>353234</td>\n",
+       "      <td>5308</td>\n",
+       "      <td>0.056748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>353233</td>\n",
+       "      <td>5308</td>\n",
+       "      <td>0.001049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>356004</td>\n",
+       "      <td>5308</td>\n",
+       "      <td>0.047884</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>354618</td>\n",
+       "      <td>5308</td>\n",
+       "      <td>0.142716</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>356006</td>\n",
+       "      <td>5308</td>\n",
+       "      <td>0.240614</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   grid_ids  hru_id_nat         w\n",
+       "0    353234        5308  0.056748\n",
+       "1    353233        5308  0.001049\n",
+       "2    356004        5308  0.047884\n",
+       "3    354618        5308  0.142716\n",
+       "4    356006        5308  0.240614"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd_wght_f = pd.read_csv(gm_wght_f)\n",
+    "wghts_id = pd_wght_f.columns[1]\n",
+    "print(f'wght_id: {wghts_id}')\n",
+    "pd_wght_f.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>grid_ids</th>\n",
+       "      <th>w</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hru_id_nat</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5308</th>\n",
+       "      <td>3548967</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5309</th>\n",
+       "      <td>353233</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5310</th>\n",
+       "      <td>1415708</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5311</th>\n",
+       "      <td>7096497</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5312</th>\n",
+       "      <td>3525380</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            grid_ids    w\n",
+       "hru_id_nat               \n",
+       "5308         3548967  1.0\n",
+       "5309          353233  1.0\n",
+       "5310         1415708  1.0\n",
+       "5311         7096497  1.0\n",
+       "5312         3525380  1.0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wght_sum = pd_wght_f.groupby(wghts_id).sum()\n",
+    "wght_sum.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "wght_sum.w.plot()\n",
+    "ax = plt.gca()\n",
+    "ax.ticklabel_format(useOffset=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ftmax = list(p_gmr.glob('tmax*.nc'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<defs>\n",
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+       "<title>Show/Hide data repr</title>\n",
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+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
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+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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+       " */\n",
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+       ".xr-wrap {\n",
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+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><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-ca7900d0-04ef-4c2b-9c47-95f2c05a42cd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ca7900d0-04ef-4c2b-9c47-95f2c05a42cd' 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'>day</span>: 31</li><li><span class='xr-has-index'>lat</span>: 585</li><li><span class='xr-has-index'>lon</span>: 1386</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e1b5151b-5106-45cf-a7d0-e9e5d8a4b086' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e1b5151b-5106-45cf-a7d0-e9e5d8a4b086' class='xr-section-summary' >Coordinates: <span>(3)</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'>day</span></div><div class='xr-var-dims'>(day)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-01 ... 2018-01-31</div><input id='attrs-53e78e45-2816-4d26-bf39-3d4265d41778' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53e78e45-2816-4d26-bf39-3d4265d41778' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48b7ba94-d98d-4107-a52b-1af32535b2e7' class='xr-var-data-in' type='checkbox'><label for='data-48b7ba94-d98d-4107-a52b-1af32535b2e7' 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>description :</span></dt><dd>days since 1900-01-01</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>365</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-03T00:00:00.000000000&#x27;, &#x27;2018-01-04T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-05T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-07T00:00:00.000000000&#x27;, &#x27;2018-01-08T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-09T00:00:00.000000000&#x27;, &#x27;2018-01-10T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-11T00:00:00.000000000&#x27;, &#x27;2018-01-12T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-13T00:00:00.000000000&#x27;, &#x27;2018-01-14T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-15T00:00:00.000000000&#x27;, &#x27;2018-01-16T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-17T00:00:00.000000000&#x27;, &#x27;2018-01-18T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-19T00:00:00.000000000&#x27;, &#x27;2018-01-20T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-21T00:00:00.000000000&#x27;, &#x27;2018-01-22T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-23T00:00:00.000000000&#x27;, &#x27;2018-01-24T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-25T00:00:00.000000000&#x27;, &#x27;2018-01-26T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-27T00:00:00.000000000&#x27;, &#x27;2018-01-28T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-29T00:00:00.000000000&#x27;, &#x27;2018-01-30T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>49.4 49.36 49.32 ... 25.11 25.07</div><input id='attrs-39b0a8ef-9bbf-4591-aee3-487f1c38f7ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39b0a8ef-9bbf-4591-aee3-487f1c38f7ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43706d46-fb7d-4ae5-b23c-884d051cf3aa' class='xr-var-data-in' type='checkbox'><label for='data-43706d46-fb7d-4ae5-b23c-884d051cf3aa' 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>units :</span></dt><dd>degrees_north</dd><dt><span>description :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>585</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><pre class='xr-var-data'>array([49.4     , 49.358333, 49.316667, ..., 25.15    , 25.108333, 25.066667])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-124.8 -124.7 ... -67.1 -67.06</div><input id='attrs-aa3989dd-d44d-4eb1-a4df-2b56bda20317' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa3989dd-d44d-4eb1-a4df-2b56bda20317' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc42fd30-bfb1-407c-96c0-017c731427cb' class='xr-var-data-in' type='checkbox'><label for='data-fc42fd30-bfb1-407c-96c0-017c731427cb' 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>units :</span></dt><dd>degrees_east</dd><dt><span>description :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>1386</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><pre class='xr-var-data'>array([-124.766667, -124.725   , -124.683333, ...,  -67.141667,  -67.1     ,\n",
+       "        -67.058333])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-2b29f095-8b8b-4b08-9d88-70ee607eaa5b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2b29f095-8b8b-4b08-9d88-70ee607eaa5b' class='xr-section-summary' >Data variables: <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>daily_maximum_temperature</span></div><div class='xr-var-dims'>(day, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-66478d7c-15ee-43e8-8e75-52ac58edd2fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66478d7c-15ee-43e8-8e75-52ac58edd2fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8d245ec-d06b-42d9-94b5-9f2dcfec03c6' class='xr-var-data-in' type='checkbox'><label for='data-e8d245ec-d06b-42d9-94b5-9f2dcfec03c6' 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>units :</span></dt><dd>K</dd><dt><span>description :</span></dt><dd>Daily Maximum Temperature</dd><dt><span>long_name :</span></dt><dd>tmmx</dd><dt><span>standard_name :</span></dt><dd>tmmx</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 61  98 231]</dd></dl></div><pre class='xr-var-data'>[25135110 values with dtype=float32]</pre></li></ul></div></li><li class='xr-section-item'><input id='section-3e553b75-5453-4539-b4d9-108c9b9a26f0' class='xr-section-summary-in' type='checkbox'  ><label for='section-3e553b75-5453-4539-b4d9-108c9b9a26f0' class='xr-section-summary' >Attributes: <span>(20)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>geospatial_bounds :</span></dt><dd>POLYGON((-124.7666666333333 49.400000000000000, -124.7666666333333 25.066666666666666, -67.058333300000015 25.066666666666666, -67.058333300000015 49.400000000000000, -124.7666666333333 49.400000000000000))</dd><dt><span>geospatial_lat_min :</span></dt><dd>25.066666666666666</dd><dt><span>geospatial_lat_max :</span></dt><dd>49.400000000000006</dd><dt><span>geospatial_lon_min :</span></dt><dd>-124.76666663333334</dd><dt><span>geospatial_lon_max :</span></dt><dd>-67.05833330000002</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.041666666666666</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.041666666666666</dd><dt><span>geospatial_lat_units :</span></dt><dd>decimal_degrees north</dd><dt><span>geospatial_lon_units :</span></dt><dd>decimal_degrees east</dd><dt><span>coordinate_system :</span></dt><dd>EPSG:4326</dd><dt><span>author :</span></dt><dd>John Abatzoglou - University of Idaho, jabatzoglou@uidaho.edu</dd><dt><span>date :</span></dt><dd>02 March 2020</dd><dt><span>note1 :</span></dt><dd>The projection information for this file is: GCS WGS 1984.</dd><dt><span>note2 :</span></dt><dd>Citation: Abatzoglou, J.T., 2013, Development of gridded surface meteorological data for ecological applications and modeling, International Journal of Climatology, DOI: 10.1002/joc.3413</dd><dt><span>note3 :</span></dt><dd>Data in slices after last_permanent_slice (1-based) are considered provisional and subject to change with subsequent updates</dd><dt><span>note4 :</span></dt><dd>Data in slices after last_provisional_slice (1-based) are considered early and subject to change with subsequent updates</dd><dt><span>note5 :</span></dt><dd>Days correspond approximately to calendar days ending at midnight, Mountain Standard Time (7 UTC the next calendar day)</dd><dt><span>History :</span></dt><dd>Translated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridWriter2)\n",
+       "Original Dataset = agg_met_tmmx_1979_CurrentYear_CONUS.nc; Translation Date = 2020-05-29T23:52:21.788Z</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:                    (day: 31, lat: 585, lon: 1386)\n",
+       "Coordinates:\n",
+       "  * day                        (day) datetime64[ns] 2018-01-01 ... 2018-01-31\n",
+       "  * lat                        (lat) float64 49.4 49.36 49.32 ... 25.11 25.07\n",
+       "  * lon                        (lon) float64 -124.8 -124.7 ... -67.1 -67.06\n",
+       "Data variables:\n",
+       "    daily_maximum_temperature  (day, lat, lon) float32 ...\n",
+       "Attributes:\n",
+       "    geospatial_bounds_crs:      EPSG:4326\n",
+       "    Conventions:                CF-1.0\n",
+       "    geospatial_bounds:          POLYGON((-124.7666666333333 49.40000000000000...\n",
+       "    geospatial_lat_min:         25.066666666666666\n",
+       "    geospatial_lat_max:         49.400000000000006\n",
+       "    geospatial_lon_min:         -124.76666663333334\n",
+       "    geospatial_lon_max:         -67.05833330000002\n",
+       "    geospatial_lon_resolution:  0.041666666666666\n",
+       "    geospatial_lat_resolution:  0.041666666666666\n",
+       "    geospatial_lat_units:       decimal_degrees north\n",
+       "    geospatial_lon_units:       decimal_degrees east\n",
+       "    coordinate_system:          EPSG:4326\n",
+       "    author:                     John Abatzoglou - University of Idaho, jabatz...\n",
+       "    date:                       02 March 2020\n",
+       "    note1:                      The projection information for this file is: ...\n",
+       "    note2:                      Citation: Abatzoglou, J.T., 2013, Development...\n",
+       "    note3:                      Data in slices after last_permanent_slice (1-...\n",
+       "    note4:                      Data in slices after last_provisional_slice (...\n",
+       "    note5:                      Days correspond approximately to calendar day...\n",
+       "    History:                    Translated to CF-1.0 Conventions by Netcdf-Ja..."
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dtmax = xr.open_dataset(ftmax[0])\n",
+    "dtmax\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
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+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><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-25bcf1ce-fc85-4a8d-a331-4d9a38d07cf3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-25bcf1ce-fc85-4a8d-a331-4d9a38d07cf3' 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'>hruid</span>: 765</li><li><span class='xr-has-index'>time</span>: 31</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8c1f77bc-12ad-4e23-b433-0002dec4f549' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8c1f77bc-12ad-4e23-b433-0002dec4f549' class='xr-section-summary' >Coordinates: <span>(2)</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'>2018-01-01 ... 2018-01-31</div><input id='attrs-941caa46-19d5-4c51-a083-569b089ff963' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-941caa46-19d5-4c51-a083-569b089ff963' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5dd480fc-3f55-46b1-a254-cfa6adf3f017' class='xr-var-data-in' type='checkbox'><label for='data-5dd480fc-3f55-46b1-a254-cfa6adf3f017' 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>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-03T00:00:00.000000000&#x27;, &#x27;2018-01-04T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-05T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-07T00:00:00.000000000&#x27;, &#x27;2018-01-08T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-09T00:00:00.000000000&#x27;, &#x27;2018-01-10T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-11T00:00:00.000000000&#x27;, &#x27;2018-01-12T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-13T00:00:00.000000000&#x27;, &#x27;2018-01-14T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-15T00:00:00.000000000&#x27;, &#x27;2018-01-16T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-17T00:00:00.000000000&#x27;, &#x27;2018-01-18T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-19T00:00:00.000000000&#x27;, &#x27;2018-01-20T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-21T00:00:00.000000000&#x27;, &#x27;2018-01-22T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-23T00:00:00.000000000&#x27;, &#x27;2018-01-24T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-25T00:00:00.000000000&#x27;, &#x27;2018-01-26T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-27T00:00:00.000000000&#x27;, &#x27;2018-01-28T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-29T00:00:00.000000000&#x27;, &#x27;2018-01-30T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>hruid</span></div><div class='xr-var-dims'>(hruid)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>5308 5309 5310 ... 7250 7251 7252</div><input id='attrs-38e4bce7-0c8a-477c-aee6-d26e79bf3ff8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38e4bce7-0c8a-477c-aee6-d26e79bf3ff8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d3ee9fb-49cc-43f4-8178-1d88715a6e40' class='xr-var-data-in' type='checkbox'><label for='data-0d3ee9fb-49cc-43f4-8178-1d88715a6e40' 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>cf_role :</span></dt><dd>timeseries_id</dd><dt><span>long_name :</span></dt><dd>local model hru id</dd></dl></div><pre class='xr-var-data'>array([5308, 5309, 5310, ..., 7250, 7251, 7252])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-805b3d86-a36c-4cfc-bb92-3f336e2ad763' class='xr-section-summary-in' type='checkbox'  checked><label for='section-805b3d86-a36c-4cfc-bb92-3f336e2ad763' class='xr-section-summary' >Data variables: <span>(8)</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>hru_lat</span></div><div class='xr-var-dims'>(hruid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2a9bcd61-d5c5-4ed4-ac1d-ae22fb28bafe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a9bcd61-d5c5-4ed4-ac1d-ae22fb28bafe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c96d67d5-a379-4516-9fcc-6c9ba5cc1eae' class='xr-var-data-in' type='checkbox'><label for='data-c96d67d5-a379-4516-9fcc-6c9ba5cc1eae' 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 of HRU centroid</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>hru_latitude</dd></dl></div><pre class='xr-var-data'>array([38.76478 , 38.82182 , 38.813427, ..., 41.94075 , 41.85711 , 42.014782],\n",
+       "      dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>hru_lon</span></div><div class='xr-var-dims'>(hruid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9270df9c-8db3-4840-8854-84ea23418326' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9270df9c-8db3-4840-8854-84ea23418326' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ea4be9a-f634-4c22-9d25-71a70c5b87d0' class='xr-var-data-in' type='checkbox'><label for='data-1ea4be9a-f634-4c22-9d25-71a70c5b87d0' 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 of HRU centroid</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>hru_longitude</dd></dl></div><pre class='xr-var-data'>array([-75.20022 , -75.22588 , -75.207695, ..., -74.698845, -74.75073 ,\n",
+       "       -74.48483 ], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>tmax</span></div><div class='xr-var-dims'>(time, hruid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-713a5855-f9e8-4a77-9fb6-8ed69febce78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-713a5855-f9e8-4a77-9fb6-8ed69febce78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfc263ca-fc57-483b-a1db-fd8d8fff10cf' class='xr-var-data-in' type='checkbox'><label for='data-bfc263ca-fc57-483b-a1db-fd8d8fff10cf' 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>Maximum daily air temperature</dd><dt><span>units :</span></dt><dd>degree_Celsius</dd><dt><span>standard_name :</span></dt><dd>maximum_daily_air_temperature</dd><dt><span>fill_value :</span></dt><dd>9.969209968386869e+36</dd></dl></div><pre class='xr-var-data'>array([[ -4.855952,  -5.049988,  -4.968304, ..., -11.512044, -11.640639,\n",
+       "        -13.070898],\n",
+       "       [ -2.907288,  -3.049988,  -2.963339, ...,  -7.959719,  -8.02132 ,\n",
+       "         -9.029993],\n",
+       "       [  1.581922,   1.350006,   1.426543, ...,  -6.888692,  -6.893773,\n",
+       "         -7.56679 ],\n",
+       "       ...,\n",
+       "       [  5.874712,   5.649994,   5.65514 , ...,   2.131379,   2.505827,\n",
+       "          0.878208],\n",
+       "       [  2.14905 ,   1.950012,   2.031696, ...,  -3.094399,  -2.965378,\n",
+       "         -4.166798],\n",
+       "       [  3.435457,   3.25    ,   3.400412, ...,  -3.749472,  -4.131615,\n",
+       "         -4.310261]], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>tmin</span></div><div class='xr-var-dims'>(time, hruid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5fc25f3d-5425-46ba-9637-d65a4e26dfc4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5fc25f3d-5425-46ba-9637-d65a4e26dfc4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b642b37-6e23-4a88-bed4-b0d3f598ba5b' class='xr-var-data-in' type='checkbox'><label for='data-2b642b37-6e23-4a88-bed4-b0d3f598ba5b' 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>Minimum daily air temperature</dd><dt><span>units :</span></dt><dd>degree_Celsius</dd><dt><span>standard_name :</span></dt><dd>minimum_daily_air_temperature</dd><dt><span>fill_value :</span></dt><dd>9.969209968386869e+36</dd></dl></div><pre class='xr-var-data'>array([[-12.226718, -11.949982, -11.73619 , ..., -23.947765, -23.370634,\n",
+       "        -24.743708],\n",
+       "       [-13.241695, -12.850006, -12.557172, ..., -21.771986, -21.690039,\n",
+       "        -22.033325],\n",
+       "       [-12.8903  , -12.449982, -12.088622, ..., -20.837439, -20.702576,\n",
+       "        -20.414734],\n",
+       "       ...,\n",
+       "       [  1.116267,   1.25    ,   1.318908, ...,  -7.39661 ,  -6.504303,\n",
+       "         -7.94037 ],\n",
+       "       [ -6.642731,  -6.449982,  -6.304738, ..., -13.494139, -13.244386,\n",
+       "        -13.833344],\n",
+       "       [ -6.449242,  -6.449982,  -6.370802, ..., -13.080064, -12.710181,\n",
+       "        -13.754098]], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>prcp</span></div><div class='xr-var-dims'>(time, hruid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-58def7df-781d-4d25-9a8a-946c9a767bc7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58def7df-781d-4d25-9a8a-946c9a767bc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86bf97ce-77ad-4284-96b2-5a30df514a1c' class='xr-var-data-in' type='checkbox'><label for='data-86bf97ce-77ad-4284-96b2-5a30df514a1c' 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 Accumulated Precipitation</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>standard_name :</span></dt><dd>prcp</dd><dt><span>fill_value :</span></dt><dd>9.969209968386869e+36</dd></dl></div><pre class='xr-var-data'>array([[ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,  0.      ],\n",
+       "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,  0.      ],\n",
+       "       [16.432114, 15.900001, 14.42156 , ...,  0.      ,  0.      ,  0.      ],\n",
+       "       ...,\n",
+       "       [ 7.065431,  6.7     ,  6.697878, ...,  0.      ,  0.      ,  0.      ],\n",
+       "       [ 0.349376,  0.5     ,  0.487225, ...,  0.65423 ,  0.94117 ,  0.      ],\n",
+       "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,  0.      ]],\n",
+       "      dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>rhmax</span></div><div class='xr-var-dims'>(time, hruid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-99e0d18a-92e8-487b-aab2-dd1e1e2ce2b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99e0d18a-92e8-487b-aab2-dd1e1e2ce2b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fa89159-c2a2-42d3-834c-7ac6559fc5d0' class='xr-var-data-in' type='checkbox'><label for='data-9fa89159-c2a2-42d3-834c-7ac6559fc5d0' 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 Relative Humidity</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>standard_name :</span></dt><dd>rhmax</dd><dt><span>fill_value :</span></dt><dd>9.969209968386869e+36</dd></dl></div><pre class='xr-var-data'>array([[ 70.36148 ,  69.4     ,  70.29976 , ..., 100.      , 100.      ,\n",
+       "         96.79829 ],\n",
+       "       [ 92.12018 ,  90.4     ,  90.458435, ..., 100.      , 100.      ,\n",
+       "        100.      ],\n",
+       "       [100.      , 100.      , 100.      , ..., 100.      , 100.      ,\n",
+       "         93.12072 ],\n",
+       "       ...,\n",
+       "       [ 86.96007 ,  86.1     ,  85.69971 , ...,  80.56381 ,  78.86359 ,\n",
+       "         70.13508 ],\n",
+       "       [ 99.12605 ,  98.      ,  97.79329 , ...,  92.67442 ,  95.29351 ,\n",
+       "         80.797676],\n",
+       "       [ 70.77223 ,  71.      ,  71.701775, ...,  53.668266,  53.163185,\n",
+       "         48.214783]], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>rhmin</span></div><div class='xr-var-dims'>(time, hruid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b505c213-08ec-4cce-a46a-2d7c115f935b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b505c213-08ec-4cce-a46a-2d7c115f935b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a16192f-be46-43ba-9760-45f7a4ffc128' class='xr-var-data-in' type='checkbox'><label for='data-5a16192f-be46-43ba-9760-45f7a4ffc128' 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 Relative Humidity</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>standard_name :</span></dt><dd>rhmin</dd><dt><span>fill_value :</span></dt><dd>9.969209968386869e+36</dd></dl></div><pre class='xr-var-data'>array([[38.24598 , 39.      , 39.99017 , ..., 42.135548, 44.718517, 40.92859 ],\n",
+       "       [40.714   , 41.5     , 42.253483, ..., 46.572147, 47.91253 , 43.313316],\n",
+       "       [47.751827, 48.4     , 48.83523 , ..., 38.753857, 40.215347, 36.932922],\n",
+       "       ...,\n",
+       "       [62.85945 , 63.7     , 63.470943, ..., 43.399376, 43.93023 , 40.739967],\n",
+       "       [52.165966, 53.      , 52.973907, ..., 45.530277, 47.015934, 42.92331 ],\n",
+       "       [31.990255, 32.4     , 32.53727 , ..., 27.716272, 28.790714, 25.545078]],\n",
+       "      dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>ws</span></div><div class='xr-var-dims'>(time, hruid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-570607b3-08b7-4c0c-9706-54a1e95f0c12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-570607b3-08b7-4c0c-9706-54a1e95f0c12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dad64c29-849d-4bc6-b90c-f07ad93e5bd3' class='xr-var-data-in' type='checkbox'><label for='data-dad64c29-849d-4bc6-b90c-f07ad93e5bd3' 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 Mean Wind Speed</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>standard_name :</span></dt><dd>ws</dd><dt><span>fill_value :</span></dt><dd>9.969209968386869e+36</dd></dl></div><pre class='xr-var-data'>array([[8.902931, 8.8     , 9.019128, ..., 8.267015, 7.879482, 7.499607],\n",
+       "       [6.635619, 6.7     , 6.842594, ..., 8.469857, 8.044371, 7.884734],\n",
+       "       [3.437283, 3.4     , 3.47387 , ..., 4.619184, 4.130554, 4.488842],\n",
+       "       ...,\n",
+       "       [7.876392, 7.6     , 7.750584, ..., 5.146002, 5.034297, 4.918589],\n",
+       "       [8.162621, 8.1     , 8.176534, ..., 6.987011, 6.671772, 6.603716],\n",
+       "       [6.639932, 6.6     , 6.67387 , ..., 7.238859, 6.81444 , 6.915257]],\n",
+       "      dtype=float32)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-0c642221-9d09-466b-8ed9-a838854a0f7e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c642221-9d09-466b-8ed9-a838854a0f7e' class='xr-section-summary' >Attributes: <span>(3)</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-1.8</dd><dt><span>featureType :</span></dt><dd>timeSeries</dd><dt><span>history :</span></dt><dd></dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (hruid: 765, time: 31)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2018-01-01 2018-01-02 ... 2018-01-31\n",
+       "  * hruid    (hruid) int32 5308 5309 5310 5311 5312 ... 7248 7249 7250 7251 7252\n",
+       "Data variables:\n",
+       "    hru_lat  (hruid) float32 ...\n",
+       "    hru_lon  (hruid) float32 ...\n",
+       "    tmax     (time, hruid) float32 ...\n",
+       "    tmin     (time, hruid) float32 ...\n",
+       "    prcp     (time, hruid) float32 ...\n",
+       "    rhmax    (time, hruid) float32 ...\n",
+       "    rhmin    (time, hruid) float32 ...\n",
+       "    ws       (time, hruid) float32 ...\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.8\n",
+       "    featureType:  timeSeries\n",
+       "    history:      "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = xr.open_dataset(p_gm / 'climate_2020_05_29.nc' )\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
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+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'hruid'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>hruid</span>: 765</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f5b32b38-85d9-4db8-92a8-c7a4ae01c2c1' class='xr-array-in' type='checkbox' ><label for='section-f5b32b38-85d9-4db8-92a8-c7a4ae01c2c1' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>5308 5309 5310 5311 5312 5313 5314 ... 7247 7248 7249 7250 7251 7252</span></div><pre class='xr-array-data'>array([5308, 5309, 5310, ..., 7250, 7251, 7252])</pre></div></li><li class='xr-section-item'><input id='section-10655a6c-89df-4edf-b6d8-798214d26807' class='xr-section-summary-in' type='checkbox'  checked><label for='section-10655a6c-89df-4edf-b6d8-798214d26807' 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'>hruid</span></div><div class='xr-var-dims'>(hruid)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>5308 5309 5310 ... 7250 7251 7252</div><input id='attrs-63762803-2cd4-4e79-8f72-c04759e4d29c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63762803-2cd4-4e79-8f72-c04759e4d29c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-289f266d-2eb0-4c65-98e3-aa17863052ca' class='xr-var-data-in' type='checkbox'><label for='data-289f266d-2eb0-4c65-98e3-aa17863052ca' 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>cf_role :</span></dt><dd>timeseries_id</dd><dt><span>long_name :</span></dt><dd>local model hru id</dd></dl></div><pre class='xr-var-data'>array([5308, 5309, 5310, ..., 7250, 7251, 7252])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-dbe34dd4-4bdf-43b7-9dfa-df4e575ec170' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dbe34dd4-4bdf-43b7-9dfa-df4e575ec170' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cf_role :</span></dt><dd>timeseries_id</dd><dt><span>long_name :</span></dt><dd>local model hru id</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'hruid' (hruid: 765)>\n",
+       "array([5308, 5309, 5310, ..., 7250, 7251, 7252])\n",
+       "Coordinates:\n",
+       "  * hruid    (hruid) int32 5308 5309 5310 5311 5312 ... 7248 7249 7250 7251 7252\n",
+       "Attributes:\n",
+       "    cf_role:    timeseries_id\n",
+       "    long_name:  local model hru id"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds.hruid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>LAYER</th>\n",
+       "      <th>region</th>\n",
+       "      <th>hru_id_nat</th>\n",
+       "      <th>nhm_id</th>\n",
+       "      <th>model_idx</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>tmax</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Unknown Area Type</td>\n",
+       "      <td>02</td>\n",
+       "      <td>5308</td>\n",
+       "      <td>5308</td>\n",
+       "      <td>173</td>\n",
+       "      <td>MULTIPOLYGON (((-75.13599 38.75503, -75.13564 ...</td>\n",
+       "      <td>-4.855952</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Unknown Area Type</td>\n",
+       "      <td>02</td>\n",
+       "      <td>5309</td>\n",
+       "      <td>5309</td>\n",
+       "      <td>175</td>\n",
+       "      <td>POLYGON ((-75.21891 38.82734, -75.21861 38.827...</td>\n",
+       "      <td>-5.049988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Unknown Area Type</td>\n",
+       "      <td>02</td>\n",
+       "      <td>5310</td>\n",
+       "      <td>5310</td>\n",
+       "      <td>174</td>\n",
+       "      <td>POLYGON ((-75.16132 38.78953, -75.16142 38.789...</td>\n",
+       "      <td>-4.968304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Unknown Area Type</td>\n",
+       "      <td>02</td>\n",
+       "      <td>5311</td>\n",
+       "      <td>5311</td>\n",
+       "      <td>176</td>\n",
+       "      <td>MULTIPOLYGON (((-75.25613 38.73334, -75.25579 ...</td>\n",
+       "      <td>-5.256260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Unknown Area Type</td>\n",
+       "      <td>02</td>\n",
+       "      <td>5312</td>\n",
+       "      <td>5312</td>\n",
+       "      <td>177</td>\n",
+       "      <td>POLYGON ((-75.22050 38.83309, -75.22084 38.833...</td>\n",
+       "      <td>-5.095961</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>760</th>\n",
+       "      <td>Unknown Area Type</td>\n",
+       "      <td>02</td>\n",
+       "      <td>7248</td>\n",
+       "      <td>7248</td>\n",
+       "      <td>209</td>\n",
+       "      <td>POLYGON ((-74.59461 42.04596, -74.59432 42.045...</td>\n",
+       "      <td>-11.845064</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>761</th>\n",
+       "      <td>Unknown Area Type</td>\n",
+       "      <td>02</td>\n",
+       "      <td>7249</td>\n",
+       "      <td>7249</td>\n",
+       "      <td>184</td>\n",
+       "      <td>POLYGON ((-74.49984 42.24245, -74.49992 42.242...</td>\n",
+       "      <td>-11.406086</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>762</th>\n",
+       "      <td>Unknown Area Type</td>\n",
+       "      <td>02</td>\n",
+       "      <td>7250</td>\n",
+       "      <td>7250</td>\n",
+       "      <td>216</td>\n",
+       "      <td>POLYGON ((-74.59760 41.92733, -74.59795 41.927...</td>\n",
+       "      <td>-11.512044</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>763</th>\n",
+       "      <td>Unknown Area Type</td>\n",
+       "      <td>02</td>\n",
+       "      <td>7251</td>\n",
+       "      <td>7251</td>\n",
+       "      <td>221</td>\n",
+       "      <td>MULTIPOLYGON (((-74.76498 41.88257, -74.76534 ...</td>\n",
+       "      <td>-11.640639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>764</th>\n",
+       "      <td>Unknown Area Type</td>\n",
+       "      <td>02</td>\n",
+       "      <td>7252</td>\n",
+       "      <td>7252</td>\n",
+       "      <td>448</td>\n",
+       "      <td>POLYGON ((-74.46415 42.02282, -74.46423 42.022...</td>\n",
+       "      <td>-13.070898</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>765 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 LAYER region  hru_id_nat  nhm_id  model_idx  \\\n",
+       "0    Unknown Area Type     02        5308    5308        173   \n",
+       "1    Unknown Area Type     02        5309    5309        175   \n",
+       "2    Unknown Area Type     02        5310    5310        174   \n",
+       "3    Unknown Area Type     02        5311    5311        176   \n",
+       "4    Unknown Area Type     02        5312    5312        177   \n",
+       "..                 ...    ...         ...     ...        ...   \n",
+       "760  Unknown Area Type     02        7248    7248        209   \n",
+       "761  Unknown Area Type     02        7249    7249        184   \n",
+       "762  Unknown Area Type     02        7250    7250        216   \n",
+       "763  Unknown Area Type     02        7251    7251        221   \n",
+       "764  Unknown Area Type     02        7252    7252        448   \n",
+       "\n",
+       "                                              geometry       tmax  \n",
+       "0    MULTIPOLYGON (((-75.13599 38.75503, -75.13564 ...  -4.855952  \n",
+       "1    POLYGON ((-75.21891 38.82734, -75.21861 38.827...  -5.049988  \n",
+       "2    POLYGON ((-75.16132 38.78953, -75.16142 38.789...  -4.968304  \n",
+       "3    MULTIPOLYGON (((-75.25613 38.73334, -75.25579 ...  -5.256260  \n",
+       "4    POLYGON ((-75.22050 38.83309, -75.22084 38.833...  -5.095961  \n",
+       "..                                                 ...        ...  \n",
+       "760  POLYGON ((-74.59461 42.04596, -74.59432 42.045... -11.845064  \n",
+       "761  POLYGON ((-74.49984 42.24245, -74.49992 42.242... -11.406086  \n",
+       "762  POLYGON ((-74.59760 41.92733, -74.59795 41.927... -11.512044  \n",
+       "763  MULTIPOLYGON (((-74.76498 41.88257, -74.76534 ... -11.640639  \n",
+       "764  POLYGON ((-74.46415 42.02282, -74.46423 42.022... -13.070898  \n",
+       "\n",
+       "[765 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf['tmax'] = 0.0\n",
+    "for index, row in gdf.iterrows():\n",
+    "    hind = row['hru_id_nat']\n",
+    "    value = ds.tmax.values[0,index]\n",
+    "    gdf.at[index,'tmax'] = value\n",
+    "gdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x16a5cead948>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "gdf.plot(column='tmax',vmin=-15, vmax=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x16a5cf6e848>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import datetime as dt\n",
+    "lvls = np.arange(-15, 0, 0.5)\n",
+    "date = dt.datetime(year=2018,month=1,day=1)\n",
+    "xrtmax = dtmax.daily_maximum_temperature.sel(day=date)-273.15\n",
+    "xrtmax.plot(levels=lvls, cmap='viridis')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, ax = plt.subplots(2, figsize=(12,12))\n",
+    "gdf.plot(ax=ax[0], column = 'tmax',linewidth=0., edgecolor='white')\n",
+    "temp = dtmax.daily_maximum_temperature[0,:,:]\n",
+    "# print(temp)\n",
+    "# print(temp.lat)\n",
+    "delaware = temp.where((dtmax.lon>=-77) & (dtmax.lon<=-74) & (dtmax.lat>=38) & (dtmax.lat<=43), drop=True)-273.15\n",
+    "# print(delaware)\n",
+    "lvs = np.arange(gdf['tmax'].min(), gdf['tmax'].max(), 0.5)\n",
+    "p=delaware.plot(ax=ax[1], levels=lvs, cmap='viridis')\n",
+    "gdf.geometry.boundary.plot(ax=ax[1], color=None, edgecolor='k',linewidth = 0.1)\n",
+    "ax[1].set_aspect('equal','box')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": 4
+}
diff --git a/gridmetetl/etl.py b/gridmetetl/etl.py
index 482fb67..eb19668 100644
--- a/gridmetetl/etl.py
+++ b/gridmetetl/etl.py
@@ -48,7 +48,8 @@ def __init__(self, climsource='GridMetSS'):
                 'rhmin': 'daily_minimum_relative_humidity',
                 'ws': 'daily_mean_wind_speed',
                 'srad': 'daily_mean_shortwave_radiation_at_surface'}
-        self.vars = None
+        self.partial = False
+        self.vars = ['tmax', 'tmin', 'ppt', 'rhmax', 'rhmin', 'ws']
 
         # type of retrieval (days) retrieve by previous number of days - used in operational mode
         # or (date) used to retrieve specific period of time
@@ -124,6 +125,14 @@ def __init__(self, climsource='GridMetSS'):
         # Starting date based on numdays
         self.str_start = None
 
+        self.dstmax = None
+        self.dstmin = None
+        self.dsrhmax = None
+        self.dsrhmin = None
+        self.dsws = None
+        self.dssrad = None
+        self.dsppt = None
+
     def write_extract_file(self, ivar, incfile, url, params):
         file = requests.get(url, params=params)
         file.raise_for_status()
@@ -133,7 +142,7 @@ def write_extract_file(self, ivar, incfile, url, params):
             fh.write(file.content)
         fh.close()
 
-    def initialize(self, ivars, iptpath, optpath, weights_file, etype=None, days=None,
+    def initialize(self, partial, ivars, iptpath, optpath, weights_file, etype=None, days=None,
                    start_date=None, end_date=None, fileprefix=''):
         """
         Initialize the fp_ohm class:
@@ -152,7 +161,9 @@ def initialize(self, ivars, iptpath, optpath, weights_file, etype=None, days=Non
 
         :return: success or failure
         """
-        self.vars = ivars
+        self.partial = partial
+        for var in ivars:
+            self.vars.append(var)
         self.iptpath = Path(iptpath)
         if self.iptpath.exists():
             print(f'input path exists {self.iptpath}', flush=True)
@@ -206,43 +217,43 @@ def initialize(self, ivars, iptpath, optpath, weights_file, etype=None, days=Non
                     self.str_start, url, params = get_gm_url(self.type, 'tmax', self.numdays,
                                                              self.start_date, self.end_date)
                     self.write_extract_file(var, ncfile, url, params)
-
+                    self.dstmax = xr.open_dataset(ncfile[-1])
                 elif var == 'tmin':
                     # Minimum Temperature
                     self.str_start, url, params = get_gm_url(self.type, 'tmin', self.numdays,
                                                              self.start_date, self.end_date)
                     self.write_extract_file(var, ncfile, url, params)
-
+                    self.dstmin = xr.open_dataset(ncfile[-1])
                 elif var == 'ppt':
                     # Precipitation
                     self.str_start, url, params = get_gm_url(self.type, 'ppt', self.numdays,
                                                              self.start_date, self.end_date)
                     self.write_extract_file(var, ncfile, url, params)
-
+                    self.dsppt = xr.open_dataset(ncfile[-1])
                 elif var == 'rhmax':
                     # Maximum Relative Humidity
                     self.str_start, url, params = get_gm_url(self.type, 'rhmax', self.numdays,
                                                              self.start_date, self.end_date)
                     self.write_extract_file(var, ncfile, url, params)
-
+                    self.dsrhmax = xr.open_dataset(ncfile[-1])
                 elif var == 'rhmin':
                     # Minimum Relative Humidity
                     self.str_start, url, params = get_gm_url(self.type, 'rhmin', self.numdays,
                                                              self.start_date, self.end_date)
                     self.write_extract_file(var, ncfile, url, params)
-
+                    self.dsrhmin = xr.open_dataset(ncfile[-1])
                 elif var == 'ws':
                     # Mean daily Wind Speed
                     self.str_start, url, params = get_gm_url(self.type, 'ws', self.numdays,
                                                              self.start_date, self.end_date)
                     self.write_extract_file(var, ncfile, url, params)
-
+                    self.dsws = xr.open_dataset(ncfile[-1])
                 elif var == 'srad':
                     # Surface downwelling shortwave flux in air
                     self.str_start, url, params = get_gm_url(self.type, 'srad', self.numdays,
                                                              self.start_date, self.end_date)
                     self.write_extract_file(var, ncfile, url, params)
-
+                    self.dssrad = xr.open_dataset(ncfile[-1])
             except HTTPError as http_err:
                 print(f'HTTP error occured: {http_err}', flush=True)
                 if self.numdays == 1:
@@ -253,13 +264,13 @@ def initialize(self, ivars, iptpath, optpath, weights_file, etype=None, days=Non
                 sys.exit(f'Other error occured: {err}')
             else:
                 print(f'Gridmet variable {var} retrieved: {ncfile[-1]}', flush=True)
-        self.ds = xr.open_mfdataset(ncfile, combine='by_coords')
+        # self.ds = xr.open_mfdataset(ncfile, combine='by_coords')
 
-        self.lat_h = self.ds['lat']
-        self.lon_h = self.ds['lon']
-        self.time_h = self.ds['day']
+        self.lat_h = self.dstmax['lat']
+        self.lon_h = self.dstmax['lon']
+        self.time_h = self.dstmax['day']
 
-        ts = self.ds.sizes
+        ts = self.dstmax.sizes
         self.dayshape = ts['day']
         self.lonshape = ts['lon']
         self.latshape = ts['lat']
@@ -306,125 +317,106 @@ def run_weights(self):
 
         for day in arange(self.numdays):
             print(f'Processing day: {day}', flush=True)
-            if 'tmax' in self.vars:
-                tvar = 'tmax'
-                d_tmax = zeros(self.num_hru)
-                d_flt_tmax = self.ds[self.gmss_vars[tvar]].values[day, :, :].flatten(order='K')
-            if 'tmin' in self.vars:
-                tvar = 'tmin'
-                d_tmin = zeros(self.num_hru)
-                d_flt_tmin = self.ds[self.gmss_vars[tvar]].values[day, :, :].flatten(order='K')
-            if 'ppt' in self.vars:
-                tvar = 'ppt'
-                d_ppt = zeros(self.num_hru)
-                d_flt_ppt = self.ds[self.gmss_vars[tvar]].values[day, :, :].flatten(order='K')
-            if 'rhmax' in self.vars:
-                tvar = 'rhmax'
-                d_rhmax = zeros(self.num_hru)
-                d_flt_rhmax = self.ds[self.gmss_vars[tvar]].values[day, :, :].flatten(order='K')
-            if 'rhmin' in self.vars:
-                tvar = 'rhmin'
-                d_rhmin = zeros(self.num_hru)
-                d_flt_rhmin = self.ds[self.gmss_vars[tvar]].values[day, :, :].flatten(order='K')
-            if 'ws' in self.vars:
-                tvar = 'ws'
-                d_ws = zeros(self.num_hru)
-                d_flt_ws = self.ds[self.gmss_vars[tvar]].values[day, :, :].flatten(order='K')
+
+            d_tmax = zeros(self.num_hru)
+            d_flt_tmax = self.dstmax[self.gmss_vars['tmax']].values[day, :, :].flatten(order='K')
+
+            d_tmin = zeros(self.num_hru)
+            d_flt_tmin = self.dstmin[self.gmss_vars['tmin']].values[day, :, :].flatten(order='K')
+
+            d_ppt = zeros(self.num_hru)
+            d_flt_ppt = self.dsppt[self.gmss_vars['ppt']].values[day, :, :].flatten(order='K')
+
+            d_rhmax = zeros(self.num_hru)
+            d_flt_rhmax = self.dsrhmax[self.gmss_vars['rhmax']].values[day, :, :].flatten(order='K')
+
+            d_rhmin = zeros(self.num_hru)
+            d_flt_rhmin = self.dsrhmin[self.gmss_vars['rhmin']].values[day, :, :].flatten(order='K')
+
+            d_ws = zeros(self.num_hru)
+            d_flt_ws = self.dsws[self.gmss_vars['ws']].values[day, :, :].flatten(order='K')
+
             if 'srad' in self.vars:
-                tvar = 'srad'
                 d_srad = zeros(self.num_hru)
-                d_flt_srad = self.ds[self.gmss_vars[tvar]].values[day, :, :].flatten(order='K')
+                d_flt_srad = self.dssrad[self.gmss_vars['srad']].values[day, :, :].flatten(order='K')
 
             for i in arange(len(tindex)):
-
-                try:
+                if not self.partial:
                     weight_id_rows = self.unique_hru_ids.get_group(tindex[i])
                     tw = weight_id_rows.w.values
                     tgid = weight_id_rows.grid_ids.values
-                    # if one var is nan all are nan. getaverage returns nan if 1 value is nan
-                    # np_get_wval return nan if all values are nan otherwise returns the
-                    # weighted masked val.  So assumption here is return a value for partially
-                    # weighted HRUs
-                    if 'tmax' in self.vars:
-                        tmpval = getaverage(d_flt_tmax[tgid]-273.15, tw)
-                        if np.isnan(tmpval):
-                            d_tmax[i] = np_get_wval(d_flt_tmax[tgid]-273.15, tw, tindex[i])
-                        else:
-                            d_tmax[i] = tmpval
-                    if 'tmin' in self.vars:
-                        tmpval = getaverage(d_flt_tmin[tgid]-273.15, tw)
-                        if np.isnan(tmpval):
-                            d_tmin[i] = np_get_wval(d_flt_tmin[tgid]-273.15, tw, tindex[i])
-                        else:
-                            d_tmin[i] = tmpval
-                    if 'ppt' in self.vars:
-                        tmpval = getaverage(d_flt_ppt[tgid], tw)
-                        if np.isnan(tmpval):
-                            d_ppt[i] = np_get_wval(d_flt_ppt[tgid], tw, tindex[i])
-                        else:
-                            d_ppt[i] = tmpval
-                    if 'rhmax' in self.vars:
-                        tmpval = getaverage(d_flt_rhmax[tgid], tw)
-                        if np.isnan(tmpval):
-                            d_rhmax[i] = np_get_wval(d_flt_rhmax[tgid], tw, tindex[i])
-                        else:
-                            d_rhmax[i] = tmpval
-                    if 'rhmin' in self.vars:
-                        tmpval = getaverage(d_flt_rhmin[tgid], tw)
-                        if np.isnan(tmpval):
-                            d_rhmin[i] = np_get_wval(d_flt_rhmin[tgid], tw, tindex[i])
-                        else:
-                            d_rhmin[i] = tmpval
-                    if 'ws' in self.vars:
-                        tmpval = getaverage(d_flt_ws[tgid], tw)
-                        if np.isnan(tmpval):
-                            d_ws[i] = np_get_wval(d_flt_ws[tgid], tw, tindex[i])
-                        else:
-                            d_ws[i] = tmpval
+                    # tmask, tgid, tw = getweights(index[i], gid, hid, w)
+
+                    d_tmax[i] = getaverage(d_flt_tmax[tgid] - 273.15, tw)
+                    d_tmin[i] = getaverage(d_flt_tmin[tgid] - 273.15, tw)
+                    d_ppt[i] = getaverage(d_flt_ppt[tgid], tw)
+                    d_rhmax[i] = getaverage(d_flt_rhmax[tgid], tw)
+                    d_rhmin[i] = getaverage(d_flt_rhmin[tgid], tw)
+                    d_ws[i] = getaverage(d_flt_ws[tgid], tw)
+
                     if 'srad' in self.vars:
-                        tmpval = getaverage(d_flt_srad[tgid], tw)
-                        if np.isnan(tmpval):
-                            d_srad[i] = np_get_wval(d_flt_srad[tgid], tw, tindex[i])
+                        d_srad[i] = getaverage(d_flt_srad[tgid], tw)
+                else:
+                    try:
+                        weight_id_rows = self.unique_hru_ids.get_group(tindex[i])
+                        tw = weight_id_rows.w.values
+                        tgid = weight_id_rows.grid_ids.values
+                        # if one var is nan all are nan. getaverage returns nan if 1 value is nan
+                        # np_get_wval return nan if all values are nan otherwise returns the
+                        # weighted masked val.  So assumption here is return a value for partially
+                        # weighted HRUs
+                        nanvar = False
+                        if np.isnan(getaverage(d_flt_tmax[tgid], tw)):
+                            nanvar = True
+
+                        if nanvar:
+                            d_tmax[i] = np_get_wval(d_flt_tmax[tgid] - 273.15, tgid, tw, tindex[i])
+                            d_tmin[i] = np_get_wval(d_flt_tmin[tgid] - 273.15, tgid, tw, tindex[i])
+                            d_ppt[i] = np_get_wval(d_flt_ppt[tgid], tgid, tw, tindex[i])
+                            d_rhmax[i] = np_get_wval(d_flt_rhmax[tgid], tgid, tw, tindex[i])
+                            d_rhmin[i] = np_get_wval(d_flt_rhmin[tgid], tgid, tw, tindex[i])
+                            d_ws[i] = np_get_wval(d_flt_ws[tgid], tgid, tw, tindex[i])
+
                         else:
-                            d_srad[i] = tmpval
-                except KeyError:
-                    # This except block protects against HRUs that are completely
-                    # outside the footprint of Gridmet.  If so, they will have no value
-                    # in the weights file and so return default value.
-                    if 'tmax' in self.vars:
+                            d_tmax[i] = getaverage(d_flt_tmax[tgid] - 273.15, tw)
+                            d_tmin[i] = getaverage(d_flt_tmin[tgid] - 273.15, tw)
+                            d_ppt[i] = getaverage(d_flt_ppt[tgid], tw)
+                            d_rhmax[i] = getaverage(d_flt_rhmax[tgid], tw)
+                            d_rhmin[i] = getaverage(d_flt_rhmin[tgid], tw)
+                            d_ws[i] = getaverage(d_flt_ws[tgid], tw)
+
+                        if 'srad' in self.vars:
+                            if nanvar:
+                                d_srad[i] = np_get_wval(d_flt_srad[tgid], tgid, tw, tindex[i])
+                            else:
+                                d_srad[i] = getaverage(d_flt_srad[tgid], tw)
+                    except KeyError:
+                        # This except block protects against HRUs that are completely
+                        # outside the footprint of Gridmet.  If so, they will have no value
+                        # in the weights file and so return default value.
                         d_tmax[i] = netCDF4.default_fillvals['f8']
-                    if 'tmin' in self.vars:
                         d_tmin[i] = netCDF4.default_fillvals['f8']
-                    if 'ppt' in self.vars:
                         d_ppt[i] = netCDF4.default_fillvals['f8']
-                    if 'rhmax' in self.vars:
                         d_rhmax[i] = netCDF4.default_fillvals['f8']
-                    if 'rhmin' in self.vars:
                         d_rhmin[i] = netCDF4.default_fillvals['f8']
-                    if 'ws' in self.vars:
                         d_ws[i] = netCDF4.default_fillvals['f8']
-                    if 'srad' in self.vars:
-                        d_srad[i] = netCDF4.default_fillvals['f8']
-
-                if i % 10000 == 0:
-                    print(f'    Processing hru {i}', flush=True)
-
-            if 'tmax' in self.vars:
-                self.np_tmax[day, :] = d_tmax[:]
-            if 'tmin' in self.vars:
-                self.np_tmin[day, :] = d_tmin[:]
-            if 'ppt' in self.vars:
-                self.np_ppt[day, :] = d_ppt[:]
-            if 'rhmax' in self.vars:
-                self.np_rhmax[day, :] = d_rhmax[:]
-            if 'rhmin' in self.vars:
-                self.np_rhmin[day, :] = d_rhmin[:]
-            if 'ws' in self.vars:
-                self.np_ws[day, :] = d_ws[:]
+                        if 'srad' in self.vars:
+                            d_srad[i] = netCDF4.default_fillvals['f8']
+
+                    if i % 10000 == 0:
+                        print(f'    Processing hru {i}', flush=True)
+
+
+            self.np_tmax[day, :] = d_tmax[:]
+            self.np_tmin[day, :] = d_tmin[:]
+            self.np_ppt[day, :] = d_ppt[:]
+            self.np_rhmax[day, :] = d_rhmax[:]
+            self.np_rhmin[day, :] = d_rhmin[:]
+            self.np_ws[day, :] = d_ws[:]
             if 'srad' in self.vars:
                 self.np_srad[day, :] = d_srad[:]
 
-        self.ds.close()
+        # self.dstmax.close()
 
     def finalize(self):
         print(Path.cwd(), flush=True)
diff --git a/gridmetetl/gridmet_etl.py b/gridmetetl/gridmet_etl.py
index ae4201b..414c626 100644
--- a/gridmetetl/gridmet_etl.py
+++ b/gridmetetl/gridmet_etl.py
@@ -59,11 +59,16 @@ def parser():
                            help='path/weight.csv - path/name of weight file', metavar='weight_file',
                            default=None, required=True)
 
-    my_parser.add_argument('-v', '--variables', nargs='*', type=str,
-                           help='over-ride default vars',
-                           choices=['tmax', 'tmin', 'ppt', 'rhmax', 'rhmin', 'ws', 'srad'],
-                           metavar='GridMet_Variables',
-                           default=['tmax', 'tmin', 'ppt', 'rhmax', 'rhmin', 'ws'])
+    my_parser.add_argument('-v', '--variables', nargs=1, type=str,
+                           help='Add additional variables (currently can add srad',
+                           choices=['srad'],
+                           metavar='Additional_GridMet_Variables',
+                           default=[])
+
+    my_parser.add_argument('--partial',  type=bool,
+                           help='set if you expect only partial mapping to HRU and you want the partial mapping',
+                           metavar='Expect partially mapped HRUs',
+                           default=False)
     return my_parser
 
 def args(parser):
@@ -117,6 +122,7 @@ def main(parser, args):
     extract_type = None
     file_prefix = None
     gm_vars = None
+    partial = None
 
     extract_type, numdays, startdate, enddate = get_extraction(my_parser, my_args)
     idir = my_args.inpath
@@ -124,14 +130,14 @@ def main(parser, args):
     wght_file = my_args.weightsfile
     file_prefix = get_file_prefix(args)
     gm_vars = my_args.variables
-
+    partial = my_args.partial
     print('starting Script', flush=True)
     fp = FpoNHM()
     print('instantiated', flush=True)
     # initialize(self, iptpath, optpath, weights_file, type=None, days=None, start_date=None, end_date=None)
     # ready = fp.initialize(idir, odir, wght_file, extract_type, numdays, startdate, enddate, file_prefix)
     try:
-        ready = fp.initialize(gm_vars, idir, odir, wght_file, etype=extract_type, days=numdays,
+        ready = fp.initialize(partial, gm_vars, idir, odir, wght_file, etype=extract_type, days=numdays,
                               start_date=startdate, end_date=enddate,
                               fileprefix=file_prefix)
         if ready:

From 750a4a451db3173e3954dcabd533ec49e0f1830a Mon Sep 17 00:00:00 2001
From: "Richard R. McDonald" <rmcd@usgs.gov>
Date: Fri, 29 May 2020 18:46:29 -0600
Subject: [PATCH 2/3] fixed notebook

---
 ...are_example.ipynb => Delaware_example.ipynb} | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)
 rename Examples/{Deleware_example.ipynb => Delaware_example.ipynb} (92%)

diff --git a/Examples/Deleware_example.ipynb b/Examples/Delaware_example.ipynb
similarity index 92%
rename from Examples/Deleware_example.ipynb
rename to Examples/Delaware_example.ipynb
index 4d37902..89349b7 100644
--- a/Examples/Deleware_example.ipynb
+++ b/Examples/Delaware_example.ipynb
@@ -275,12 +275,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "text/plain": [
+       "(0.999, 1.001)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -294,7 +304,8 @@
    "source": [
     "wght_sum.w.plot()\n",
     "ax = plt.gca()\n",
-    "ax.ticklabel_format(useOffset=False)"
+    "ax.ticklabel_format(useOffset=False)\n",
+    "ax.set_ylim(.999, 1.001)"
    ]
   },
   {

From fa841e81426872f6f3563550efe4deec4d03132d Mon Sep 17 00:00:00 2001
From: "Richard R. McDonald" <rmcd@usgs.gov>
Date: Wed, 10 Jun 2020 12:55:14 -0600
Subject: [PATCH 3/3] notebook for Haily

---
 Examples/Delaware_xarray.ipynb | 474 +++++++++++++++++++++++++++++++++
 1 file changed, 474 insertions(+)
 create mode 100644 Examples/Delaware_xarray.ipynb

diff --git a/Examples/Delaware_xarray.ipynb b/Examples/Delaware_xarray.ipynb
new file mode 100644
index 0000000..498cab6
--- /dev/null
+++ b/Examples/Delaware_xarray.ipynb
@@ -0,0 +1,474 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\rmcd\\anaconda3\\envs\\gmetl\\lib\\site-packages\\xarray\\conventions.py:494: SerializationWarning: variable 'air_temperature' has _Unsigned attribute but is not of integer type. Ignoring attribute.\n",
+      "  use_cftime=use_cftime,\n"
+     ]
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "from pathlib import Path\n",
+    "\n",
+    "ds = xr.open_dataset(Path('C:/Users/rmcd/Downloads/tmmx_2016.nc'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
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+       "<title>Show/Hide data repr</title>\n",
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+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
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+       "</svg>\n",
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+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
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+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
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+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
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+       "\n",
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+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
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+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
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+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
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+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
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+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
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+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
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+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
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+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
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+       "</style><div class='xr-wrap'><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-46e4643f-d4c4-4947-8745-bd53f4b48283' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-46e4643f-d4c4-4947-8745-bd53f4b48283' 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'>crs</span>: 1</li><li><span class='xr-has-index'>day</span>: 366</li><li><span class='xr-has-index'>lat</span>: 585</li><li><span class='xr-has-index'>lon</span>: 1386</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b2fd4687-cdfb-4287-abf8-0c7142468dc5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b2fd4687-cdfb-4287-abf8-0c7142468dc5' class='xr-section-summary' >Coordinates: <span>(4)</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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-124.8 -124.7 ... -67.1 -67.06</div><input id='attrs-42ef5a63-0373-4650-9c73-34a5aed2cae7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42ef5a63-0373-4650-9c73-34a5aed2cae7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11a801ce-26d9-4f5c-aca0-1205fb1b433d' class='xr-var-data-in' type='checkbox'><label for='data-11a801ce-26d9-4f5c-aca0-1205fb1b433d' 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>units :</span></dt><dd>degrees_east</dd><dt><span>description :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><pre class='xr-var-data'>array([-124.766667, -124.725   , -124.683333, ...,  -67.141667,  -67.1     ,\n",
+       "        -67.058333])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>49.4 49.36 49.32 ... 25.11 25.07</div><input id='attrs-3dd4d721-1b32-4aea-b0a3-b82f1319542d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3dd4d721-1b32-4aea-b0a3-b82f1319542d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76b23b21-2a59-4bd4-a111-26b326dee569' class='xr-var-data-in' type='checkbox'><label for='data-76b23b21-2a59-4bd4-a111-26b326dee569' 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>units :</span></dt><dd>degrees_north</dd><dt><span>description :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><pre class='xr-var-data'>array([49.4     , 49.358333, 49.316667, ..., 25.15    , 25.108333, 25.066667])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>day</span></div><div class='xr-var-dims'>(day)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-01-01 ... 2016-12-31</div><input id='attrs-9672fca6-5ce8-4df3-b292-b996b8ff7430' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9672fca6-5ce8-4df3-b292-b996b8ff7430' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-284f3064-54b0-4510-9948-e9459a7749ee' class='xr-var-data-in' type='checkbox'><label for='data-284f3064-54b0-4510-9948-e9459a7749ee' 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>description :</span></dt><dd>days since 1900-01-01</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2016-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-01-03T00:00:00.000000000&#x27;, ..., &#x27;2016-12-29T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-12-30T00:00:00.000000000&#x27;, &#x27;2016-12-31T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>crs</span></div><div class='xr-var-dims'>(crs)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>3</div><input id='attrs-bbab2d0e-8b40-4064-a0a2-627656831754' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbab2d0e-8b40-4064-a0a2-627656831754' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-149665c0-1c3e-4581-bb7d-2f59b24790ea' class='xr-var-data-in' type='checkbox'><label for='data-149665c0-1c3e-4581-bb7d-2f59b24790ea' 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>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>long_name :</span></dt><dd>WGS 84</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>GeoTransform :</span></dt><dd>-124.7666666333333 0.041666666666666 0  49.400000000000000 -0.041666666666666</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div><pre class='xr-var-data'>array([3], dtype=uint16)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-cd06c704-82c9-400c-a893-e8d23b2424d8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cd06c704-82c9-400c-a893-e8d23b2424d8' class='xr-section-summary' >Data variables: <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>air_temperature</span></div><div class='xr-var-dims'>(day, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-13fd7e97-d049-423d-8ae9-ea0bf71c7eeb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13fd7e97-d049-423d-8ae9-ea0bf71c7eeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28f4fb2f-d90c-41df-b4e0-695ca81f8d03' class='xr-var-data-in' type='checkbox'><label for='data-28f4fb2f-d90c-41df-b4e0-695ca81f8d03' 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>units :</span></dt><dd>K</dd><dt><span>description :</span></dt><dd>Daily Maximum Temperature</dd><dt><span>long_name :</span></dt><dd>tmmx</dd><dt><span>standard_name :</span></dt><dd>tmmx</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd></dl></div><pre class='xr-var-data'>[296756460 values with dtype=float32]</pre></li></ul></div></li><li class='xr-section-item'><input id='section-eb241d3f-d83f-41de-a81f-ccb5d84ae753' class='xr-section-summary-in' type='checkbox'  ><label for='section-eb241d3f-d83f-41de-a81f-ccb5d84ae753' class='xr-section-summary' >Attributes: <span>(19)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>geospatial_bounds :</span></dt><dd>POLYGON((-124.7666666333333 49.400000000000000, -124.7666666333333 25.066666666666666, -67.058333300000015 25.066666666666666, -67.058333300000015 49.400000000000000, -124.7666666333333 49.400000000000000))</dd><dt><span>geospatial_lat_min :</span></dt><dd>25.066666666666666</dd><dt><span>geospatial_lat_max :</span></dt><dd>49.40000000000000</dd><dt><span>geospatial_lon_min :</span></dt><dd>-124.7666666333333</dd><dt><span>geospatial_lon_max :</span></dt><dd>-67.058333300000015</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.041666666666666</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.041666666666666</dd><dt><span>geospatial_lat_units :</span></dt><dd>decimal_degrees north</dd><dt><span>geospatial_lon_units :</span></dt><dd>decimal_degrees east</dd><dt><span>coordinate_system :</span></dt><dd>EPSG:4326</dd><dt><span>author :</span></dt><dd>John Abatzoglou - University of Idaho, jabatzoglou@uidaho.edu</dd><dt><span>date :</span></dt><dd>04 July 2019</dd><dt><span>note1 :</span></dt><dd>The projection information for this file is: GCS WGS 1984.</dd><dt><span>note2 :</span></dt><dd>Citation: Abatzoglou, J.T., 2013, Development of gridded surface meteorological data for ecological applications and modeling, International Journal of Climatology, DOI: 10.1002/joc.3413</dd><dt><span>note3 :</span></dt><dd>Data in slices after last_permanent_slice (1-based) are considered provisional and subject to change with subsequent updates</dd><dt><span>note4 :</span></dt><dd>Data in slices after last_provisional_slice (1-based) are considered early and subject to change with subsequent updates</dd><dt><span>note5 :</span></dt><dd>Days correspond approximately to calendar days ending at midnight, Mountain Standard Time (7 UTC the next calendar day)</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:          (crs: 1, day: 366, lat: 585, lon: 1386)\n",
+       "Coordinates:\n",
+       "  * lon              (lon) float64 -124.8 -124.7 -124.7 ... -67.14 -67.1 -67.06\n",
+       "  * lat              (lat) float64 49.4 49.36 49.32 49.28 ... 25.15 25.11 25.07\n",
+       "  * day              (day) datetime64[ns] 2016-01-01 2016-01-02 ... 2016-12-31\n",
+       "  * crs              (crs) uint16 3\n",
+       "Data variables:\n",
+       "    air_temperature  (day, lat, lon) float32 ...\n",
+       "Attributes:\n",
+       "    geospatial_bounds_crs:      EPSG:4326\n",
+       "    Conventions:                CF-1.6\n",
+       "    geospatial_bounds:          POLYGON((-124.7666666333333 49.40000000000000...\n",
+       "    geospatial_lat_min:         25.066666666666666\n",
+       "    geospatial_lat_max:         49.40000000000000\n",
+       "    geospatial_lon_min:         -124.7666666333333\n",
+       "    geospatial_lon_max:         -67.058333300000015\n",
+       "    geospatial_lon_resolution:  0.041666666666666\n",
+       "    geospatial_lat_resolution:  0.041666666666666\n",
+       "    geospatial_lat_units:       decimal_degrees north\n",
+       "    geospatial_lon_units:       decimal_degrees east\n",
+       "    coordinate_system:          EPSG:4326\n",
+       "    author:                     John Abatzoglou - University of Idaho, jabatz...\n",
+       "    date:                       04 July 2019\n",
+       "    note1:                      The projection information for this file is: ...\n",
+       "    note2:                      Citation: Abatzoglou, J.T., 2013, Development...\n",
+       "    note3:                      Data in slices after last_permanent_slice (1-...\n",
+       "    note4:                      Data in slices after last_provisional_slice (...\n",
+       "    note5:                      Days correspond approximately to calendar day..."
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x22bbb3cd908>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds.air_temperature.sel(day='2016-01-01').plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": 4
+}