notebooks/03_NSRDB_introduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NREL National Solar Radiation Database (NSRDB) - HSDS Demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrates basic usage of the National Renewable Energy Laboratory (NREL) National Solar Radiation Database (NSRDB) data. The data is provided from Amazon Web Services using the HDF Group's Highly Scalable Data Service (HSDS).\n",
    "\n",
    "Please consult the README file for setup instructions prior to running this notebook.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T19:48:39.175801Z",
     "start_time": "2019-02-06T19:48:39.034304Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import h5pyd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "from scipy.spatial import cKDTree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Usage\n",
    "\n",
    "The NSRDB is provided in annual .h5 files and currently spans 1998-2018.  \n",
    "Each year can be accessed from /nrel/nsrdb/nsrdb_${year}.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T18:32:02.178393Z",
     "start_time": "2019-02-06T18:32:01.822243Z"
    }
   },
   "outputs": [],
   "source": [
    "# Open the desired year of nsrdb data\n",
    "# server endpoint, username, password is found via a config file\n",
    "f = h5pyd.File(\"/nrel/nsrdb/v3/nsrdb_2012.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T18:32:04.318312Z",
     "start_time": "2019-02-06T18:32:03.824155Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Version']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(f.attrs)  # list attributes belonging to the root group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T18:32:09.277364Z",
     "start_time": "2019-02-06T18:32:09.032427Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.0.6'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.attrs['Version']   # attributes can be used to provide desriptions of the content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T18:33:57.735172Z",
     "start_time": "2019-02-06T18:33:57.373693Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/air_temperature (17568, 2018392)\n",
      "/alpha (17568, 2018392)\n",
      "/aod (17568, 2018392)\n",
      "/asymmetry (17568, 2018392)\n",
      "/cld_opd_dcomp (17568, 2018392)\n",
      "/cld_reff_dcomp (17568, 2018392)\n",
      "/clearsky_dhi (17568, 2018392)\n",
      "/clearsky_dni (17568, 2018392)\n",
      "/clearsky_ghi (17568, 2018392)\n",
      "/cloud_press_acha (17568, 2018392)\n",
      "/cloud_type (17568, 2018392)\n",
      "/coordinates (2018392, 2)\n",
      "/dew_point (17568, 2018392)\n",
      "/dhi (17568, 2018392)\n",
      "/dni (17568, 2018392)\n",
      "/fill_flag (17568, 2018392)\n",
      "/ghi (17568, 2018392)\n",
      "/meta (2018392,)\n",
      "/ozone (17568, 2018392)\n",
      "/relative_humidity (17568, 2018392)\n",
      "/solar_zenith_angle (17568, 2018392)\n",
      "/ssa (17568, 2018392)\n",
      "/surface_albedo (17568, 2018392)\n",
      "/surface_pressure (17568, 2018392)\n",
      "/time_index (17568,)\n",
      "/total_precipitable_water (17568, 2018392)\n",
      "/wind_direction (17568, 2018392)\n",
      "/wind_speed (17568, 2018392)\n"
     ]
    }
   ],
   "source": [
    "# list the datasets in the file\n",
    "for k in f:\n",
    "    dset = f[k]\n",
    "    print(f\"{dset.name} {dset.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T18:34:32.751989Z",
     "start_time": "2019-02-06T18:34:32.630250Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17568, 2018392)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Datasets are stored in a 2d array of time x location\n",
    "dset = f['ghi']\n",
    "dset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T18:45:17.972025Z",
     "start_time": "2019-02-06T18:45:17.286944Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-01-01 00:00:00', '2012-01-01 00:30:00',\n",
       "               '2012-01-01 01:00:00', '2012-01-01 01:30:00',\n",
       "               '2012-01-01 02:00:00', '2012-01-01 02:30:00',\n",
       "               '2012-01-01 03:00:00', '2012-01-01 03:30:00',\n",
       "               '2012-01-01 04:00:00', '2012-01-01 04:30:00',\n",
       "               ...\n",
       "               '2012-12-31 19:00:00', '2012-12-31 19:30:00',\n",
       "               '2012-12-31 20:00:00', '2012-12-31 20:30:00',\n",
       "               '2012-12-31 21:00:00', '2012-12-31 21:30:00',\n",
       "               '2012-12-31 22:00:00', '2012-12-31 22:30:00',\n",
       "               '2012-12-31 23:00:00', '2012-12-31 23:30:00'],\n",
       "              dtype='datetime64[ns]', length=17568, freq=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract datetime index for datasets\n",
    "time_index = pd.to_datetime(f['time_index'][...].astype(str))\n",
    "time_index # Temporal resolution is 30min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T18:46:51.882658Z",
     "start_time": "2019-02-06T18:46:06.078162Z"
    }
   },
   "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>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>elevation</th>\n",
       "      <th>timezone</th>\n",
       "      <th>country</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>urban</th>\n",
       "      <th>population</th>\n",
       "      <th>landcover</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-19.99</td>\n",
       "      <td>-175.259995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>-9999</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-19.99</td>\n",
       "      <td>-175.220001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>-9999</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-19.99</td>\n",
       "      <td>-175.179993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>-9999</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-19.99</td>\n",
       "      <td>-175.139999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>-9999</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-19.99</td>\n",
       "      <td>-175.100006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>-9999</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   latitude   longitude  elevation  timezone  country    state   county  \\\n",
       "0    -19.99 -175.259995        0.0        13  b'None'  b'None'  b'None'   \n",
       "1    -19.99 -175.220001        0.0        13  b'None'  b'None'  b'None'   \n",
       "2    -19.99 -175.179993        0.0        13  b'None'  b'None'  b'None'   \n",
       "3    -19.99 -175.139999        0.0        13  b'None'  b'None'  b'None'   \n",
       "4    -19.99 -175.100006        0.0        13  b'None'  b'None'  b'None'   \n",
       "\n",
       "     urban  population  landcover  \n",
       "0  b'None'       -9999        210  \n",
       "1  b'None'       -9999        210  \n",
       "2  b'None'       -9999        210  \n",
       "3  b'None'       -9999        210  \n",
       "4  b'None'       -9999        210  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Locational information is stored in either 'meta' or 'coordinates'\n",
    "meta = pd.DataFrame(f['meta'][...])\n",
    "meta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T18:47:46.899747Z",
     "start_time": "2019-02-06T18:47:46.896662Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int16')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ghi dataset have been saved as integers\n",
    "dset.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T18:47:51.066179Z",
     "start_time": "2019-02-06T18:47:51.062426Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.918221312"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset.shape[0] * dset.shape[1] * 2 * 10**-9 # 70 GB per dataset!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T18:47:57.377851Z",
     "start_time": "2019-02-06T18:47:57.374273Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2688, 372)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset.chunks # Chunked by week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T18:47:58.010112Z",
     "start_time": "2019-02-06T18:47:58.005677Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9998719999999999"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset.chunks[0] * dset.chunks[1] * 2 * 10**-6 # 2 MB per chunk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T18:49:17.786073Z",
     "start_time": "2019-02-06T18:49:17.671184Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To convert dataset values back to floats use the 'psm_scale_factor'\n",
    "dset.attrs['psm_scale_factor'] # Irradiance values have been truncated to integer precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T18:52:13.089310Z",
     "start_time": "2019-02-06T18:52:12.342907Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wind_speed scale factor =  10.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.9"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# wind speed on the other hand has single decimal percision when scaled by 10\n",
    "scale_factor = f['wind_speed'].attrs['psm_scale_factor']\n",
    "#. units = f['wind_speed'].attrs['psm_units']. # tbd - this attribute doesn't exist\n",
    "print('wind_speed scale factor = ', scale_factor)\n",
    "# print('wind_speed units after unscaling = ', units)\n",
    "f['wind_speed'][0, 0] / scale_factor # divide by scale_factor to return native value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time-slicing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the time_index from the server and convert to a pandas DatetimeIndex for convenience:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T18:57:48.925869Z",
     "start_time": "2019-02-06T18:57:47.990623Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-01-01 00:00:00', '2012-01-01 00:30:00',\n",
       "               '2012-01-01 01:00:00', '2012-01-01 01:30:00',\n",
       "               '2012-01-01 02:00:00', '2012-01-01 02:30:00',\n",
       "               '2012-01-01 03:00:00', '2012-01-01 03:30:00',\n",
       "               '2012-01-01 04:00:00', '2012-01-01 04:30:00',\n",
       "               ...\n",
       "               '2012-12-31 19:00:00', '2012-12-31 19:30:00',\n",
       "               '2012-12-31 20:00:00', '2012-12-31 20:30:00',\n",
       "               '2012-12-31 21:00:00', '2012-12-31 21:30:00',\n",
       "               '2012-12-31 22:00:00', '2012-12-31 22:30:00',\n",
       "               '2012-12-31 23:00:00', '2012-12-31 23:30:00'],\n",
       "              dtype='datetime64[ns]', length=17568, freq=None)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_index = pd.to_datetime(f['time_index'][...].astype(str))\n",
    "time_index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract indexes for a particular span of time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T19:00:14.646068Z",
     "start_time": "2019-02-06T19:00:14.641059Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2880, 2881, 2882, ..., 4365, 4366, 4367])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "march = time_index.month == 3\n",
    "np.where(march)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or a particular date:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T19:16:18.978254Z",
     "start_time": "2019-02-06T19:16:18.973874Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8880"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timestep = np.where(time_index == '2012-07-04 00:00:00')[0][0]\n",
    "timestep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Map Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T19:16:28.086246Z",
     "start_time": "2019-02-06T19:16:24.814153Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'description': '(latitude, longitude)'}\n"
     ]
    }
   ],
   "source": [
    "# Extract coordinates (lat, lon)\n",
    "print(dict(f['coordinates'].attrs))\n",
    "coords = f['coordinates'][...]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T19:30:40.930339Z",
     "start_time": "2019-02-06T19:30:37.902430Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.86 ms, sys: 0 ns, total: 6.86 ms\n",
      "Wall time: 1min 47s\n"
     ]
    }
   ],
   "source": [
    "dset = f['ghi']\n",
    "%time data = dset[timestep, ::10]   # extract every 10th location at a particular time\n",
    "df = pd.DataFrame() # Combine data with coordinates in a DataFrame\n",
    "df['longitude'] = coords[::10, 1]\n",
    "df['latitude'] = coords[::10, 0]\n",
    "df['ghi'] = data / dset.attrs['psm_scale_factor'] # unscale dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T19:30:40.935891Z",
     "start_time": "2019-02-06T19:30:40.932229Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(201840, 3)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T19:30:50.703645Z",
     "start_time": "2019-02-06T19:30:44.870411Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='longitude', y='latitude', c='ghi',\n",
    "                colormap='YlOrRd',\n",
    "                title=str(time_index[timestep]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T19:38:03.467178Z",
     "start_time": "2019-02-06T19:38:03.343285Z"
    }
   },
   "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>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>elevation</th>\n",
       "      <th>timezone</th>\n",
       "      <th>country</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>urban</th>\n",
       "      <th>population</th>\n",
       "      <th>landcover</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70276</th>\n",
       "      <td>32.529999</td>\n",
       "      <td>-117.099998</td>\n",
       "      <td>55.062500</td>\n",
       "      <td>-8</td>\n",
       "      <td>b'United States'</td>\n",
       "      <td>b'California'</td>\n",
       "      <td>b'San Diego'</td>\n",
       "      <td>b'None'</td>\n",
       "      <td>32326</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70588</th>\n",
       "      <td>32.570000</td>\n",
       "      <td>-117.099998</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>-8</td>\n",
       "      <td>b'United States'</td>\n",
       "      <td>b'California'</td>\n",
       "      <td>b'San Diego'</td>\n",
       "      <td>b'Tijuana'</td>\n",
       "      <td>27971</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70589</th>\n",
       "      <td>32.570000</td>\n",
       "      <td>-117.059998</td>\n",
       "      <td>24.920000</td>\n",
       "      <td>-8</td>\n",
       "      <td>b'United States'</td>\n",
       "      <td>b'California'</td>\n",
       "      <td>b'San Diego'</td>\n",
       "      <td>b'Tijuana'</td>\n",
       "      <td>51608</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70590</th>\n",
       "      <td>32.570000</td>\n",
       "      <td>-117.019997</td>\n",
       "      <td>96.599998</td>\n",
       "      <td>-8</td>\n",
       "      <td>b'United States'</td>\n",
       "      <td>b'California'</td>\n",
       "      <td>b'San Diego'</td>\n",
       "      <td>b'Tijuana'</td>\n",
       "      <td>15236</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70591</th>\n",
       "      <td>32.570000</td>\n",
       "      <td>-116.980003</td>\n",
       "      <td>140.600006</td>\n",
       "      <td>-8</td>\n",
       "      <td>b'United States'</td>\n",
       "      <td>b'California'</td>\n",
       "      <td>b'San Diego'</td>\n",
       "      <td>b'Tijuana'</td>\n",
       "      <td>2949</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        latitude   longitude   elevation  timezone           country  \\\n",
       "70276  32.529999 -117.099998   55.062500        -8  b'United States'   \n",
       "70588  32.570000 -117.099998    7.100000        -8  b'United States'   \n",
       "70589  32.570000 -117.059998   24.920000        -8  b'United States'   \n",
       "70590  32.570000 -117.019997   96.599998        -8  b'United States'   \n",
       "70591  32.570000 -116.980003  140.600006        -8  b'United States'   \n",
       "\n",
       "               state        county       urban  population  landcover  \n",
       "70276  b'California'  b'San Diego'     b'None'       32326        130  \n",
       "70588  b'California'  b'San Diego'  b'Tijuana'       27971        190  \n",
       "70589  b'California'  b'San Diego'  b'Tijuana'       51608        190  \n",
       "70590  b'California'  b'San Diego'  b'Tijuana'       15236        110  \n",
       "70591  b'California'  b'San Diego'  b'Tijuana'        2949        130  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Full resolution subset of Colorado\n",
    "meta = pd.DataFrame(f['meta'][...])\n",
    "CA = meta.loc[meta['state'] == b'California'] # Note .h5 saves strings as bit-strings\n",
    "CA.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T19:38:14.050394Z",
     "start_time": "2019-02-06T19:38:09.966272Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 15.4 ms, sys: 0 ns, total: 15.4 ms\n",
      "Wall time: 1min 47s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(26010, 3)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time data = dset[timestep][CA.index]  # full-resolution subset\n",
    "df = CA[['longitude', 'latitude']].copy()\n",
    "df['ghi'] = data / dset.attrs['psm_scale_factor']\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T19:38:16.260753Z",
     "start_time": "2019-02-06T19:38:15.367784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='longitude', y='latitude', c='ghi',\n",
    "                colormap='YlOrRd',\n",
    "                title=str(time_index[timestep]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nearest Timeseries for given Lat/Lon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T19:52:44.346725Z",
     "start_time": "2019-02-06T19:52:34.038821Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Site index for New York City: \t\t 1244690\n",
      "Coordinates of New York City: \t (40.7128, -74.0059)\n",
      "Coordinates of nearest point: \t [ 40.73 -74.02]\n"
     ]
    }
   ],
   "source": [
    "# Unlike the gridded WTK data the NSRDB is provided as sparse time-series dataset.\n",
    "# The quickest way to find the nearest site it using a KDtree\n",
    "\n",
    "dset_coords = f['coordinates'][...]\n",
    "tree = cKDTree(dset_coords)\n",
    "def nearest_site(tree, lat_coord, lon_coord):\n",
    "    lat_lon = np.array([lat_coord, lon_coord])\n",
    "    dist, pos = tree.query(lat_lon)\n",
    "    return pos\n",
    "\n",
    "NewYorkCity = (40.7128, -74.0059)\n",
    "NewYorkCity_idx = nearest_site(tree, NewYorkCity[0], NewYorkCity[1] )\n",
    "\n",
    "print(\"Site index for New York City: \\t\\t {}\".format(NewYorkCity_idx))\n",
    "print(\"Coordinates of New York City: \\t {}\".format(NewYorkCity))\n",
    "print(\"Coordinates of nearest point: \\t {}\".format(dset_coords[NewYorkCity_idx]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T19:53:48.160502Z",
     "start_time": "2019-02-06T19:53:46.607119Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 518 μs, sys: 3.14 ms, total: 3.66 ms\n",
      "Wall time: 693 ms\n"
     ]
    }
   ],
   "source": [
    "# Get the entire 2012 timeseries data for a point in NYC\n",
    "%time tseries = dset[:, NewYorkCity_idx] / dset.attrs['psm_scale_factor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T19:53:48.165562Z",
     "start_time": "2019-02-06T19:53:48.162324Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17568"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tseries)   # 1 years * 365 days * 24 hours * 30 minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T19:54:20.235635Z",
     "start_time": "2019-02-06T19:54:20.063966Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'NYC ghi in 2012')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(time_index, tseries)\n",
    "plt.ylabel(\"ghi\")\n",
    "plt.title(\"NYC ghi in 2012\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GHI Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T19:58:14.770799Z",
     "start_time": "2019-02-06T19:58:14.726103Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>hour</th>\n",
       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>191.806452</td>\n",
       "      <td>290.774194</td>\n",
       "      <td>314.822581</td>\n",
       "      <td>328.016129</td>\n",
       "      <td>299.096774</td>\n",
       "      <td>224.919355</td>\n",
       "      <td>150.903226</td>\n",
       "      <td>48.370968</td>\n",
       "      <td>0.387097</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>280.068966</td>\n",
       "      <td>356.431034</td>\n",
       "      <td>410.603448</td>\n",
       "      <td>429.120690</td>\n",
       "      <td>407.586207</td>\n",
       "      <td>353.517241</td>\n",
       "      <td>230.068966</td>\n",
       "      <td>113.689655</td>\n",
       "      <td>17.482759</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>377.451613</td>\n",
       "      <td>488.935484</td>\n",
       "      <td>528.177419</td>\n",
       "      <td>553.048387</td>\n",
       "      <td>539.645161</td>\n",
       "      <td>462.306452</td>\n",
       "      <td>352.483871</td>\n",
       "      <td>219.112903</td>\n",
       "      <td>81.096774</td>\n",
       "      <td>1.838710</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>568.500000</td>\n",
       "      <td>680.566667</td>\n",
       "      <td>697.900000</td>\n",
       "      <td>685.133333</td>\n",
       "      <td>627.966667</td>\n",
       "      <td>561.566667</td>\n",
       "      <td>422.916667</td>\n",
       "      <td>293.516667</td>\n",
       "      <td>147.283333</td>\n",
       "      <td>25.700000</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.580645</td>\n",
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       "      <td>563.596774</td>\n",
       "      <td>581.967742</td>\n",
       "      <td>634.758065</td>\n",
       "      <td>616.290323</td>\n",
       "      <td>546.241935</td>\n",
       "      <td>431.596774</td>\n",
       "      <td>325.645161</td>\n",
       "      <td>183.983871</td>\n",
       "      <td>71.096774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.500000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>602.450000</td>\n",
       "      <td>664.683333</td>\n",
       "      <td>723.700000</td>\n",
       "      <td>677.850000</td>\n",
       "      <td>606.800000</td>\n",
       "      <td>579.633333</td>\n",
       "      <td>468.983333</td>\n",
       "      <td>338.783333</td>\n",
       "      <td>200.316667</td>\n",
       "      <td>92.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.048387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>572.790323</td>\n",
       "      <td>699.177419</td>\n",
       "      <td>719.612903</td>\n",
       "      <td>715.532258</td>\n",
       "      <td>646.532258</td>\n",
       "      <td>604.596774</td>\n",
       "      <td>495.548387</td>\n",
       "      <td>370.564516</td>\n",
       "      <td>226.983871</td>\n",
       "      <td>93.225806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>562.032258</td>\n",
       "      <td>634.000000</td>\n",
       "      <td>671.564516</td>\n",
       "      <td>690.048387</td>\n",
       "      <td>647.725806</td>\n",
       "      <td>556.048387</td>\n",
       "      <td>448.274194</td>\n",
       "      <td>313.483871</td>\n",
       "      <td>163.693548</td>\n",
       "      <td>42.693548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>468.150000</td>\n",
       "      <td>555.883333</td>\n",
       "      <td>604.966667</td>\n",
       "      <td>559.650000</td>\n",
       "      <td>553.516667</td>\n",
       "      <td>473.583333</td>\n",
       "      <td>347.800000</td>\n",
       "      <td>207.400000</td>\n",
       "      <td>74.933333</td>\n",
       "      <td>2.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>271.209677</td>\n",
       "      <td>325.741935</td>\n",
       "      <td>397.661290</td>\n",
       "      <td>372.354839</td>\n",
       "      <td>329.112903</td>\n",
       "      <td>261.532258</td>\n",
       "      <td>186.387097</td>\n",
       "      <td>86.548387</td>\n",
       "      <td>7.645161</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>270.050000</td>\n",
       "      <td>332.133333</td>\n",
       "      <td>353.333333</td>\n",
       "      <td>361.533333</td>\n",
       "      <td>321.416667</td>\n",
       "      <td>236.550000</td>\n",
       "      <td>135.333333</td>\n",
       "      <td>29.450000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>141.564516</td>\n",
       "      <td>209.838710</td>\n",
       "      <td>242.903226</td>\n",
       "      <td>248.741935</td>\n",
       "      <td>240.516129</td>\n",
       "      <td>175.483871</td>\n",
       "      <td>85.177419</td>\n",
       "      <td>13.806452</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "hour          0    1    2    3    4    5    6    7    8    9   ...  \\\n",
       "month                                                          ...   \n",
       "1       0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2       0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "3       0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "4       0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "5       1.580645  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "6      11.500000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "7       9.048387  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "8       0.161290  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "9       0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "10      0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "11      0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "12      0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "\n",
       "hour           14          15          16          17          18          19  \\\n",
       "month                                                                           \n",
       "1      191.806452  290.774194  314.822581  328.016129  299.096774  224.919355   \n",
       "2      280.068966  356.431034  410.603448  429.120690  407.586207  353.517241   \n",
       "3      377.451613  488.935484  528.177419  553.048387  539.645161  462.306452   \n",
       "4      568.500000  680.566667  697.900000  685.133333  627.966667  561.566667   \n",
       "5      468.274194  563.596774  581.967742  634.758065  616.290323  546.241935   \n",
       "6      602.450000  664.683333  723.700000  677.850000  606.800000  579.633333   \n",
       "7      572.790323  699.177419  719.612903  715.532258  646.532258  604.596774   \n",
       "8      562.032258  634.000000  671.564516  690.048387  647.725806  556.048387   \n",
       "9      468.150000  555.883333  604.966667  559.650000  553.516667  473.583333   \n",
       "10     271.209677  325.741935  397.661290  372.354839  329.112903  261.532258   \n",
       "11     270.050000  332.133333  353.333333  361.533333  321.416667  236.550000   \n",
       "12     141.564516  209.838710  242.903226  248.741935  240.516129  175.483871   \n",
       "\n",
       "hour           20          21          22         23  \n",
       "month                                                 \n",
       "1      150.903226   48.370968    0.387097   0.000000  \n",
       "2      230.068966  113.689655   17.482759   0.000000  \n",
       "3      352.483871  219.112903   81.096774   1.838710  \n",
       "4      422.916667  293.516667  147.283333  25.700000  \n",
       "5      431.596774  325.645161  183.983871  71.096774  \n",
       "6      468.983333  338.783333  200.316667  92.250000  \n",
       "7      495.548387  370.564516  226.983871  93.225806  \n",
       "8      448.274194  313.483871  163.693548  42.693548  \n",
       "9      347.800000  207.400000   74.933333   2.783333  \n",
       "10     186.387097   86.548387    7.645161   0.000000  \n",
       "11     135.333333   29.450000    0.000000   0.000000  \n",
       "12      85.177419   13.806452    0.000000   0.000000  \n",
       "\n",
       "[12 rows x 24 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'ghi': tseries}, index=time_index)\n",
    "df[\"year\"] = df.index.year\n",
    "df[\"month\"] = df.index.month\n",
    "df[\"day\"] = df.index.day\n",
    "df[\"hour\"] = df.index.hour\n",
    "\n",
    "agg = df.groupby([\"month\",\"hour\"]).mean()\n",
    "agg = agg.reset_index().pivot(index=\"month\",columns=\"hour\",values=\"ghi\")\n",
    "agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T19:58:30.404312Z",
     "start_time": "2019-02-06T19:58:30.214762Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x788d4d9bae00>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(agg)\n",
    "plt.xlabel(\"Hour\")\n",
    "plt.ylabel(\"Month\")\n",
    "plt.title(\"12 x 24 Mean GHI (W/m^2)\")\n",
    "plt.colorbar()"
   ]
  },
  {
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}