wholmgren/reference_forecasts_labeling.ipynb

## reference_forecasts_labeling.ipynb
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    "# Reference Forecasts\n",
    "\n",
    "This [Jupyter notebook](https://jupyter.org) is designed to introduce you to the [Solar Forecast Arbiter's](https://solarforecastarbiter.org/) built-in reference forecast capabilities. It is divided into 2 sections:\n",
    "\n",
    "1. [NWP-based forecasts](#NWP-based-forecasts)\n",
    "2. [Persistence forecasts](#persistence-forecasts)\n",
    "\n",
    "The API documentation is available [here](https://solarforecastarbiter-core.readthedocs.io/en/latest/reference_forecasts.html).\n",
    "\n",
    "Click the \">| Run\" button in the toolbar above or type shift-enter to run the code in each cell. The help menu contains a brief User Interface Tour."
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "from functools import partial\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from bokeh.core.properties import value\n",
    "from bokeh.io import output_notebook\n",
    "from bokeh.layouts import gridplot\n",
    "from bokeh.models import ColumnDataSource, HoverTool\n",
    "from bokeh.plotting import figure, show\n",
    "from bokeh.palettes import Category10_10 as PALETTE\n",
    "TOOLS = \"pan,box_zoom,xwheel_zoom,reset,save\"\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from solarforecastarbiter import datamodel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NWP-based forecasts\n",
    "\n",
    "Forecasts based on NWP model data are used for intraday and longer forecasts. The Solar Forecast Arbiter contains a set of functions to process data from NWP forecasts. Here, we explore some of the functionality with an emphasis on obtaining results. See the [NWP section of the documentation](https://solarforecastarbiter-core.readthedocs.io/en/latest/reference_forecasts.html#nwp) for additional information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `solarforecastarbiter` package includes a handful of subsetted NWP model runs for testing. We'll use these for our demonstration below. The models were all initialized at 2019-05-15 00Z and the subsets include about half a degree of latitude and longitude near Tucson, AZ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from solarforecastarbiter.io import nwp\n",
    "# find the files\n",
    "base_path = Path(nwp.__file__).resolve().parents[0] / 'tests/data'\n",
    "# define file loading function that knows where to find the files\n",
    "load_forecast = partial(nwp.load_forecast, base_path=base_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define coordinates\n",
    "latitude = 32.2\n",
    "longitude = -110.9\n",
    "elevation = 700\n",
    "\n",
    "# define initialization time, start time, end time, run time, issue time\n",
    "init_time = pd.Timestamp('20190515T0000Z')\n",
    "start = pd.Timestamp('20190515T0100Z')\n",
    "end = pd.Timestamp('20190518T0000Z')\n",
    "run_time = pd.Timestamp('20190515T0200Z')\n",
    "issue_time = pd.Timestamp('20190515T0000Z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import solarforecastarbiter.io.fetch.nwp as fetch_nwp\n",
    "from solarforecastarbiter.reference_forecasts import utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Solar Forecast Arbiter organizes its metadata and data according to the ideas described in the [data model](https://solarforecastarbiter.org/datamodel/). The `solarforecastarbiter` package implements this data model in `solarforecastarbiter.datamodel`. Here, we define a `Site` object for which to make forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a Site object\n",
    "site = datamodel.Site(\n",
    "    name='Tucson, AZ',\n",
    "    latitude=latitude,\n",
    "    longitude=longitude,\n",
    "    elevation=elevation,\n",
    "    timezone='America/Phoenix'\n",
    ")\n",
    "\n",
    "# define a Forecast object\n",
    "forecast = datamodel.Forecast(\n",
    "    name='OASIS DA GHI',\n",
    "    site=site,\n",
    "    variable='ghi',\n",
    "    interval_label='instant',\n",
    "    interval_value_type='mean',\n",
    "    interval_length='1h',\n",
    "    issue_time_of_day=datetime.time(hour=0),\n",
    "    run_length=pd.Timedelta('24h'),\n",
    "    lead_time_to_start=pd.Timedelta('0h')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we select a NWP+post processing model, load and process the data, and plot the processed data.\n",
    "\n",
    "Here we use the subhourly irradiance directly from the HRRR model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the relevant python modules\n",
    "from solarforecastarbiter.reference_forecasts import main, models\n",
    "\n",
    "# select the model\n",
    "model = models.hrrr_subhourly_to_subhourly_instantaneous\n",
    "\n",
    "# tell the model where to find the NWP data\n",
    "model_wrapped = partial(model, load_forecast=load_forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time\n",
       "2019-05-15 00:00:00+00:00     418.0\n",
       "2019-05-15 00:15:00+00:00     395.0\n",
       "2019-05-15 00:30:00+00:00     324.0\n",
       "2019-05-15 00:45:00+00:00     270.0\n",
       "2019-05-15 01:00:00+00:00     212.0\n",
       "                              ...  \n",
       "2019-05-15 17:00:00+00:00     880.0\n",
       "2019-05-15 17:15:00+00:00     920.0\n",
       "2019-05-15 17:30:00+00:00     930.0\n",
       "2019-05-15 17:45:00+00:00     970.0\n",
       "2019-05-15 18:00:00+00:00    1000.0\n",
       "Freq: 15T, Name: ghi, Length: 73, dtype: float32"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load and process the NWP data. ac_power is None because site is not a power plant.\n",
    "ghi, dni, dhi, air_temperature, wind_speed, ac_power = main.run_nwp(forecast, model_wrapped, run_time, issue_time)\n",
    "ghi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a function that will make a plot of the processed NWP data. The details of the function are not important for this exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_nwp(ghi, dni, dhi, air_temperature, wind_speed, ac_power):\n",
    "    \"\"\"Make a plot of the variables from processed NWP forecasts\"\"\"\n",
    "    # data prep\n",
    "    if ac_power is None:\n",
    "        ac_power = pd.Series(None, index=ghi.index)\n",
    "    data = dict(zip(('GHI', 'DNI', 'DHI', 'Air temperature', 'Wind speed', 'AC power', 'index'),\n",
    "                    (ghi, dni, dhi, air_temperature, wind_speed, ac_power, ghi.index)))\n",
    "    source = ColumnDataSource(data)\n",
    "\n",
    "    figs = []\n",
    "    fig_kwargs = dict(tools=TOOLS, x_axis_type=\"datetime\", plot_height=200)\n",
    "    palette = iter(PALETTE)\n",
    "    \n",
    "    # irradiance figure\n",
    "    fig1 = figure(**fig_kwargs)\n",
    "    for name in ('GHI', 'DNI', 'DHI'):\n",
    "        fig1.line(x='index', y=name, source=source, legend=value(name), color=next(palette), line_width=2)\n",
    "    fig1.yaxis.axis_label = \"Irradiance (W/m^2)\"\n",
    "    fig1.legend.location = \"top_left\"\n",
    "    figs.append(fig1)\n",
    "\n",
    "    # air temperature figure\n",
    "    fig_kwargs['x_range'] = fig1.x_range  # link x-zoom\n",
    "    fig2 = figure(**fig_kwargs)\n",
    "    fig2.line(x='index', y='Air temperature', source=source, legend=value(\"Air temperature\"), color=next(palette), line_width=2)\n",
    "    fig2.yaxis.axis_label = \"Air temperature (C)\"\n",
    "    fig2.legend.location = \"top_left\"\n",
    "    figs.append(fig2)\n",
    "\n",
    "    # wind speed figure\n",
    "    fig3 = figure(**fig_kwargs)\n",
    "    fig3.line(x='index', y='Wind speed', source=source, legend=value(\"Wind speed\"), color=next(palette), line_width=2)\n",
    "    fig3.yaxis.axis_label = \"Wind speed (m/s)\"\n",
    "    fig3.legend.location = \"top_left\"\n",
    "    figs.append(fig3)\n",
    "\n",
    "    # ac power figure\n",
    "    if not ac_power.dropna().empty:\n",
    "        fig4 = figure(**fig_kwargs)\n",
    "        fig4.line(x='index', y='AC power', source=source, legend=value(\"AC power\"), color=next(palette), line_width=2)\n",
    "        fig4.yaxis.axis_label = \"AC power (MW)\"\n",
    "        fig4.legend.location = \"top_left\"\n",
    "        figs.append(fig4)\n",
    "    \n",
    "    figs[-1].xaxis.axis_label = 'Time (UTC)'\n",
    "\n",
    "    grid = gridplot(figs, ncols=1, plot_width=800)\n",
    "    \n",
    "    tooltips = [\n",
    "        ('timestamp', '@index{%FT%H:%M:%S%z}'),\n",
    "        ('GHI', '@GHI'),\n",
    "        ('DNI', '@DNI'),\n",
    "        ('DHI', '@DHI')\n",
    "    ]\n",
    "    formatters={'index': 'datetime'}\n",
    "    hover_kwargs = {}\n",
    "    hover = HoverTool(tooltips=tooltips, formatters=formatters, mode='vline', **hover_kwargs)\n",
    "    fig1.add_tools(hover)\n",
    "    for fig in figs:\n",
    "        fig.legend.click_policy = \"hide\"\n",
    "    return grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function to plot the HRRR data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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(C)\",\"formatter\":{\"id\":\"1152\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1127\",\"type\":\"BasicTicker\"}},\"id\":\"1126\",\"type\":\"LinearAxis\"},{\"attributes\":{\"num_minor_ticks\":5,\"tickers\":[{\"id\":\"1044\",\"type\":\"AdaptiveTicker\"},{\"id\":\"1045\",\"type\":\"AdaptiveTicker\"},{\"id\":\"1046\",\"type\":\"AdaptiveTicker\"},{\"id\":\"1047\",\"type\":\"DaysTicker\"},{\"id\":\"1048\",\"type\":\"DaysTicker\"},{\"id\":\"1049\",\"type\":\"DaysTicker\"},{\"id\":\"1050\",\"type\":\"DaysTicker\"},{\"id\":\"1051\",\"type\":\"MonthsTicker\"},{\"id\":\"1052\",\"type\":\"MonthsTicker\"},{\"id\":\"1053\",\"type\":\"MonthsTicker\"},{\"id\":\"1054\",\"type\":\"MonthsTicker\"},{\"id\":\"1055\",\"type\":\"YearsTicker\"}]},\"id\":\"1013\",\"type\":\"DatetimeTicker\"},{\"attributes\":{},\"id\":\"1127\",\"type\":\"BasicTicker\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1127\",\"type\":\"BasicTicker\"}},\"id\":\"1130\",\"type\":\"Grid\"},{\"attributes\":{\"callback\":null},\"id\":\"1115\",\"type\":\"DataRange1d\"},{\"attributes\":{\"dimensions\":\"width\"},\"id\":\"1024\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"line_color\":\"#d62728\",\"line_width\":2,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"Air temperature\"}},\"id\":\"1143\",\"type\":\"Line\"},{\"attributes\":{\"axis_label\":\"Irradiance (W/m^2)\",\"formatter\":{\"id\":\"1043\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1018\",\"type\":\"BasicTicker\"}},\"id\":\"1017\",\"type\":\"LinearAxis\"},{\"attributes\":{\"num_minor_ticks\":5,\"tickers\":[{\"id\":\"1208\",\"type\":\"AdaptiveTicker\"},{\"id\":\"1209\",\"type\":\"AdaptiveTicker\"},{\"id\":\"1210\",\"type\":\"AdaptiveTicker\"},{\"id\":\"1211\",\"type\":\"DaysTicker\"},{\"id\":\"1212\",\"type\":\"DaysTicker\"},{\"id\":\"1213\",\"type\":\"DaysTicker\"},{\"id\":\"1214\",\"type\":\"DaysTicker\"},{\"id\":\"1215\",\"type\":\"MonthsTicker\"},{\"id\":\"1216\",\"type\":\"MonthsTicker\"},{\"id\":\"1217\",\"type\":\"MonthsTicker\"},{\"id\":\"1218\",\"type\":\"MonthsTicker\"},{\"id\":\"1219\",\"type\":\"YearsTicker\"}]},\"id\":\"1177\",\"type\":\"DatetimeTicker\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1131\",\"type\":\"PanTool\"},{\"id\":\"1132\",\"type\":\"BoxZoomTool\"},{\"id\":\"1133\",\"type\":\"WheelZoomTool\"},{\"id\":\"1134\",\"type\":\"ResetTool\"},{\"id\":\"1135\",\"type\":\"SaveTool\"}]},\"id\":\"1136\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"1131\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"1018\",\"type\":\"BasicTicker\"},{\"attributes\":{\"overlay\":{\"id\":\"1165\",\"type\":\"BoxAnnotation\"}},\"id\":\"1132\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1018\",\"type\":\"BasicTicker\"}},\"id\":\"1021\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1022\",\"type\":\"PanTool\"},{\"attributes\":{\"dimensions\":\"width\"},\"id\":\"1133\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"GHI\"}},\"id\":\"1035\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1134\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"1135\",\"type\":\"SaveTool\"},{\"attributes\":{\"overlay\":{\"id\":\"1056\",\"type\":\"BoxAnnotation\"}},\"id\":\"1023\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"Air temperature\"}},\"id\":\"1144\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1190\",\"type\":\"SaveTool\"},{\"attributes\":{\"data_source\":{\"id\":\"1002\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1143\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1144\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"1146\",\"type\":\"CDSView\"}},\"id\":\"1145\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1186\",\"type\":\"PanTool\"},{\"id\":\"1187\",\"type\":\"BoxZoomTool\"},{\"id\":\"1188\",\"type\":\"WheelZoomTool\"},{\"id\":\"1189\",\"type\":\"ResetTool\"},{\"id\":\"1190\",\"type\":\"SaveTool\"}]},\"id\":\"1191\",\"type\":\"Toolbar\"},{\"attributes\":{\"source\":{\"id\":\"1002\",\"type\":\"ColumnDataSource\"}},\"id\":\"1146\",\"type\":\"CDSView\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"Wind speed\"}},\"id\":\"1199\",\"type\":\"Line\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1148\",\"type\":\"Title\"},{\"attributes\":{\"data_source\":{\"id\":\"1002\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1198\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1199\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"1201\",\"type\":\"CDSView\"}},\"id\":\"1200\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1150\",\"type\":\"DatetimeTickFormatter\"},{\"attributes\":{\"source\":{\"id\":\"1002\",\"type\":\"ColumnDataSource\"}},\"id\":\"1201\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1041\",\"type\":\"DatetimeTickFormatter\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1203\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1152\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"GHI\"}},\"id\":\"1034\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1207\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"mantissas\":[1,2,5],\"max_interval\":500.0,\"num_minor_ticks\":0},\"id\":\"1153\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"mantissas\":[1,2,5],\"max_interval\":500.0,\"num_minor_ticks\":0},\"id\":\"1208\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"max_interval\":1800000.0,\"min_interval\":1000.0,\"num_minor_ticks\":0},\"id\":\"1154\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"max_interval\":43200000.0,\"min_interval\":3600000.0,\"num_minor_ticks\":0},\"id\":\"1155\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"max_interval\":1800000.0,\"min_interval\":1000.0,\"num_minor_ticks\":0},\"id\":\"1209\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]},\"id\":\"1156\",\"type\":\"DaysTicker\"},{\"attributes\":{\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"max_interval\":43200000.0,\"min_interval\":3600000.0,\"num_minor_ticks\":0},\"id\":\"1210\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"days\":[1,4,7,10,13,16,19,22,25,28]},\"id\":\"1157\",\"type\":\"DaysTicker\"},{\"attributes\":{\"ticker\":{\"id\":\"1013\",\"type\":\"DatetimeTicker\"}},\"id\":\"1016\",\"type\":\"Grid\"},{\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]},\"id\":\"1211\",\"type\":\"DaysTicker\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"1158\",\"type\":\"DaysTicker\"},{\"attributes\":{\"days\":[1,4,7,10,13,16,19,22,25,28]},\"id\":\"1212\",\"type\":\"DaysTicker\"},{\"attributes\":{\"children\":[[{\"id\":\"1003\",\"subtype\":\"Figure\",\"type\":\"Plot\"},0,0],[{\"id\":\"1113\",\"subtype\":\"Figure\",\"type\":\"Plot\"},1,0],[{\"id\":\"1168\",\"subtype\":\"Figure\",\"type\":\"Plot\"},2,0]]},\"id\":\"1289\",\"type\":\"GridBox\"},{\"attributes\":{\"days\":[1,15]},\"id\":\"1159\",\"type\":\"DaysTicker\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"1213\",\"type\":\"DaysTicker\"},{\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]},\"id\":\"1160\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"days\":[1,15]},\"id\":\"1214\",\"type\":\"DaysTicker\"},{\"attributes\":{\"months\":[0,2,4,6,8,10]},\"id\":\"1161\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]},\"id\":\"1215\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"months\":[0,4,8]},\"id\":\"1162\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"months\":[0,2,4,6,8,10]},\"id\":\"1216\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"months\":[0,6]},\"id\":\"1163\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"months\":[0,4,8]},\"id\":\"1217\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"days\":[1,4,7,10,13,16,19,22,25,28]},\"id\":\"1048\",\"type\":\"DaysTicker\"},{\"attributes\":{},\"id\":\"1164\",\"type\":\"YearsTicker\"},{\"attributes\":{\"months\":[0,6]},\"id\":\"1218\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1165\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"1049\",\"type\":\"DaysTicker\"},{\"attributes\":{},\"id\":\"1219\",\"type\":\"YearsTicker\"},{\"attributes\":{\"click_policy\":\"hide\",\"items\":[{\"id\":\"1167\",\"type\":\"LegendItem\"}],\"location\":\"top_left\"},\"id\":\"1166\",\"type\":\"Legend\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1220\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"label\":{\"value\":\"Air temperature\"},\"renderers\":[{\"id\":\"1145\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1167\",\"type\":\"LegendItem\"},{\"attributes\":{\"click_policy\":\"hide\",\"items\":[{\"id\":\"1222\",\"type\":\"LegendItem\"}],\"location\":\"top_left\"},\"id\":\"1221\",\"type\":\"Legend\"},{\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]},\"id\":\"1051\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"below\":[{\"id\":\"1176\",\"type\":\"DatetimeAxis\"}],\"center\":[{\"id\":\"1180\",\"type\":\"Grid\"},{\"id\":\"1185\",\"type\":\"Grid\"},{\"id\":\"1221\",\"type\":\"Legend\"}],\"left\":[{\"id\":\"1181\",\"type\":\"LinearAxis\"}],\"plot_height\":200,\"plot_width\":800,\"renderers\":[{\"id\":\"1200\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1203\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1191\",\"type\":\"Toolbar\"},\"toolbar_location\":null,\"x_range\":{\"id\":\"1004\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"1172\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"1170\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1174\",\"type\":\"LinearScale\"}},\"id\":\"1168\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"label\":{\"value\":\"Wind speed\"},\"renderers\":[{\"id\":\"1200\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1222\",\"type\":\"LegendItem\"},{\"attributes\":{\"days\":[1,15]},\"id\":\"1050\",\"type\":\"DaysTicker\"},{\"attributes\":{\"line_color\":\"#9467bd\",\"line_width\":2,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"Wind speed\"}},\"id\":\"1198\",\"type\":\"Line\"},{\"attributes\":{\"callback\":null},\"id\":\"1170\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1172\",\"type\":\"LinearScale\"},{\"attributes\":{\"callback\":null,\"formatters\":{\"index\":\"datetime\"},\"mode\":\"vline\",\"tooltips\":[[\"timestamp\",\"@index{%FT%H:%M:%S%z}\"],[\"GHI\",\"@GHI\"],[\"DNI\",\"@DNI\"],[\"DHI\",\"@DHI\"]]},\"id\":\"1293\",\"type\":\"HoverTool\"},{\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]},\"id\":\"1047\",\"type\":\"DaysTicker\"},{\"attributes\":{},\"id\":\"1174\",\"type\":\"LinearScale\"},{\"attributes\":{\"axis_label\":\"Time (UTC)\",\"formatter\":{\"id\":\"1205\",\"type\":\"DatetimeTickFormatter\"},\"ticker\":{\"id\":\"1177\",\"type\":\"DatetimeTicker\"}},\"id\":\"1176\",\"type\":\"DatetimeAxis\"},{\"attributes\":{\"toolbar\":{\"id\":\"1290\",\"type\":\"ProxyToolbar\"},\"toolbar_location\":\"above\"},\"id\":\"1291\",\"type\":\"ToolbarBox\"},{\"attributes\":{\"axis_label\":\"Wind speed (m/s)\",\"formatter\":{\"id\":\"1207\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1182\",\"type\":\"BasicTicker\"}},\"id\":\"1181\",\"type\":\"LinearAxis\"},{\"attributes\":{\"below\":[{\"id\":\"1012\",\"type\":\"DatetimeAxis\"}],\"center\":[{\"id\":\"1016\",\"type\":\"Grid\"},{\"id\":\"1021\",\"type\":\"Grid\"},{\"id\":\"1057\",\"type\":\"Legend\"}],\"left\":[{\"id\":\"1017\",\"type\":\"LinearAxis\"}],\"plot_height\":200,\"plot_width\":800,\"renderers\":[{\"id\":\"1036\",\"type\":\"GlyphRenderer\"},{\"id\":\"1062\",\"type\":\"GlyphRenderer\"},{\"id\":\"1089\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1039\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1027\",\"type\":\"Toolbar\"},\"toolbar_location\":null,\"x_range\":{\"id\":\"1004\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"1008\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"1006\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1010\",\"type\":\"LinearScale\"}},\"id\":\"1003\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"1182\",\"type\":\"BasicTicker\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1182\",\"type\":\"BasicTicker\"}},\"id\":\"1185\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1205\",\"type\":\"DatetimeTickFormatter\"},{\"attributes\":{\"tools\":[{\"id\":\"1022\",\"type\":\"PanTool\"},{\"id\":\"1023\",\"type\":\"BoxZoomTool\"},{\"id\":\"1024\",\"type\":\"WheelZoomTool\"},{\"id\":\"1025\",\"type\":\"ResetTool\"},{\"id\":\"1026\",\"type\":\"SaveTool\"},{\"id\":\"1131\",\"type\":\"PanTool\"},{\"id\":\"1132\",\"type\":\"BoxZoomTool\"},{\"id\":\"1133\",\"type\":\"WheelZoomTool\"},{\"id\":\"1134\",\"type\":\"ResetTool\"},{\"id\":\"1135\",\"type\":\"SaveTool\"},{\"id\":\"1186\",\"type\":\"PanTool\"},{\"id\":\"1187\",\"type\":\"BoxZoomTool\"},{\"id\":\"1188\",\"type\":\"WheelZoomTool\"},{\"id\":\"1189\",\"type\":\"ResetTool\"},{\"id\":\"1190\",\"type\":\"SaveTool\"}]},\"id\":\"1290\",\"type\":\"ProxyToolbar\"},{\"attributes\":{},\"id\":\"1186\",\"type\":\"PanTool\"},{\"attributes\":{\"label\":{\"value\":\"DNI\"},\"renderers\":[{\"id\":\"1062\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1085\",\"type\":\"LegendItem\"},{\"attributes\":{\"overlay\":{\"id\":\"1220\",\"type\":\"BoxAnnotation\"}},\"id\":\"1187\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"dimensions\":\"width\"},\"id\":\"1188\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"1189\",\"type\":\"ResetTool\"}],\"root_ids\":[\"1292\"]},\"title\":\"Bokeh Application\",\"version\":\"1.3.4\"}};\n",
       "  var render_items = [{\"docid\":\"07ed47aa-00f1-4077-b1e0-3c380b119524\",\"roots\":{\"1292\":\"511754d7-26ce-418b-ad59-5930e95ac91e\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1292"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = plot_nwp(ghi, dni, dhi, air_temperature, wind_speed, ac_power)\n",
    "show(grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from importlib import reload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = reload(models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a Forecast object\n",
    "forecast = datamodel.Forecast(\n",
    "    name='OASIS DA GHI',\n",
    "    site=site,\n",
    "    variable='ghi',\n",
    "    interval_label='ending',\n",
    "    interval_value_type='mean',\n",
    "    interval_length='1h',\n",
    "    issue_time_of_day=datetime.time(hour=0),\n",
    "    run_length=pd.Timedelta('18h'),\n",
    "    lead_time_to_start=pd.Timedelta('0h')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time\n",
      "2019-05-15 01:00:00+00:00    317.416656\n",
      "2019-05-15 02:00:00+00:00    105.000000\n",
      "2019-05-15 03:00:00+00:00      2.166667\n",
      "2019-05-15 04:00:00+00:00      0.000000\n",
      "2019-05-15 05:00:00+00:00      0.000000\n",
      "2019-05-15 06:00:00+00:00      0.000000\n",
      "2019-05-15 07:00:00+00:00      0.000000\n",
      "2019-05-15 08:00:00+00:00      0.000000\n",
      "2019-05-15 09:00:00+00:00      0.000000\n",
      "2019-05-15 10:00:00+00:00      0.000000\n",
      "2019-05-15 11:00:00+00:00      0.000000\n",
      "2019-05-15 12:00:00+00:00      0.000000\n",
      "2019-05-15 13:00:00+00:00     11.583334\n",
      "2019-05-15 14:00:00+00:00    159.666672\n",
      "2019-05-15 15:00:00+00:00    388.000000\n",
      "2019-05-15 16:00:00+00:00    612.083313\n",
      "2019-05-15 17:00:00+00:00    798.333313\n",
      "2019-05-15 18:00:00+00:00    945.000000\n",
      "Freq: H, Name: ghi, dtype: float32\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"cf33f719-1210-4c12-8d32-789ea24f194f\" data-root-id=\"2002\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
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       "  var render_items = [{\"docid\":\"ad9d3b92-f7b4-4547-aeab-03889db2d591\",\"roots\":{\"2002\":\"cf33f719-1210-4c12-8d32-789ea24f194f\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "2002"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select the model\n",
    "model = models.hrrr_subhourly_to_hourly_mean\n",
    "\n",
    "# tell the model where to find the NWP data\n",
    "model_wrapped = partial(model, load_forecast=load_forecast)\n",
    "\n",
    "# load and process the NWP data. ac_power is None because site is not a power plant.\n",
    "ghi, dni, dhi, air_temperature, wind_speed, ac_power = main.run_nwp(forecast, model_wrapped, run_time, issue_time)\n",
    "\n",
    "print(ghi)\n",
    "\n",
    "grid = plot_nwp(ghi, dni, dhi, air_temperature, wind_speed, ac_power)\n",
    "show(grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a Forecast object\n",
    "forecast = datamodel.Forecast(\n",
    "    name='OASIS DA GHI',\n",
    "    site=site,\n",
    "    variable='ghi',\n",
    "    interval_label='beginning',\n",
    "    interval_value_type='mean',\n",
    "    interval_length='1h',\n",
    "    issue_time_of_day=datetime.time(hour=0),\n",
    "    run_length=pd.Timedelta('18h'),\n",
    "    lead_time_to_start=pd.Timedelta('0h')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time\n",
      "2019-05-15 00:00:00+00:00    334.583344\n",
      "2019-05-15 01:00:00+00:00    120.500000\n",
      "2019-05-15 02:00:00+00:00      4.333333\n",
      "2019-05-15 03:00:00+00:00      0.000000\n",
      "2019-05-15 04:00:00+00:00      0.000000\n",
      "2019-05-15 05:00:00+00:00      0.000000\n",
      "2019-05-15 06:00:00+00:00      0.000000\n",
      "2019-05-15 07:00:00+00:00      0.000000\n",
      "2019-05-15 08:00:00+00:00      0.000000\n",
      "2019-05-15 09:00:00+00:00      0.000000\n",
      "2019-05-15 10:00:00+00:00      0.000000\n",
      "2019-05-15 11:00:00+00:00      0.000000\n",
      "2019-05-15 12:00:00+00:00      7.416667\n",
      "2019-05-15 13:00:00+00:00    142.083328\n",
      "2019-05-15 14:00:00+00:00    368.750000\n",
      "2019-05-15 15:00:00+00:00    593.916687\n",
      "2019-05-15 16:00:00+00:00    784.166687\n",
      "2019-05-15 17:00:00+00:00    935.000000\n",
      "Freq: H, Name: ghi, dtype: float32\n"
     ]
    },
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     "output_type": "display_data"
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   "source": [
    "# select the model\n",
    "model = models.hrrr_subhourly_to_hourly_mean\n",
    "\n",
    "# tell the model where to find the NWP data\n",
    "model_wrapped = partial(model, load_forecast=load_forecast)\n",
    "\n",
    "# load and process the NWP data. ac_power is None because site is not a power plant.\n",
    "ghi, dni, dhi, air_temperature, wind_speed, ac_power = main.run_nwp(forecast, model_wrapped, run_time, issue_time)\n",
    "\n",
    "print(ghi)\n",
    "\n",
    "grid = plot_nwp(ghi, dni, dhi, air_temperature, wind_speed, ac_power)\n",
    "show(grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hour-ending 13 has about the same ~10 W/m2 GHI forecast as Hour-beginning 12. The subhourly forecast had the first non-0 forecast at 12:45Z, so it's all consistent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Solar Forecast Arbiter includes processing functions derive GHI from a NWP model's cloud cover forecast and estimate the DNI and DHI using the [Erbs model](https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.irradiance.erbs.html?highlight=erbs). These functions are applied to data from the GFS, NAM, and RAP weather models. \n",
    "\n",
    "For example, GFS data is processed as follows:\n",
    "\n",
    "1. Load time series forecast of cloud cover, air temperature, and wind speed from gridded data.\n",
    "2. Convert mixed interval cloud cover in raw data into standard intervals.\n",
    "2. Interpolate cloud cover, air temperature, wind speed to 5 minute resolution to minimize errors due to solar position.\n",
    "2. Compute GHI from cloud cover.\n",
    "2. Compute DNI and DHI from GHI.\n",
    "2. If required, calculate AC power.\n",
    "2. Resample 5 minute data to 1 hour data.\n",
    "\n",
    "See the `models` [documentation](https://solarforecastarbiter-core.readthedocs.io/en/latest/generated/solarforecastarbiter.reference_forecasts.models.html#module-solarforecastarbiter.reference_forecasts.models) for more explanation. The plot below shows the results of these processing steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pvlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "oasis = pvlib.iotools.read_midc_raw_data_from_nrel('UAT', pd.Timestamp('201905150000Z'), pd.Timestamp('201905180000Z'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Year', 'DOY', 'MST', 'Direct Normal [W/m^2]',\n",
       "       'Diffuse Horiz [W/m^2]', 'Global Horiz (tracker) [W/m^2]',\n",
       "       'Global Horiz (platform) [W/m^2]', 'Temp CHP1 [deg C]',\n",
       "       'Temp CM22 (tracker) [deg C]', 'Temp CM22 (platform) [deg C]',\n",
       "       'CR1000 Temp [deg C]', 'CR1000 Battery [VDC]',\n",
       "       'Air Temperature [deg C]', 'Rel Humidity [%]',\n",
       "       'Station Pressure [mBar]', 'Avg Wind Speed @ 3m [m/s]',\n",
       "       'Avg Wind Direction @ 3m [deg from N]', 'Peak Wind Speed @ 3m [m/s]'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oasis.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_vars = [\n",
    "    'Global Horiz (platform) [W/m^2]', \n",
    "    'Direct Normal [W/m^2]', \n",
    "    'Diffuse Horiz [W/m^2]', \n",
    "    'Air Temperature [deg C]',\n",
    "    'Avg Wind Speed @ 3m [m/s]'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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oXG9BQB+F61G4fkFAMzMzMzOTQUCuR+F6FK5BQOF6FK5HwUFAMzMzMzPTQUCamZmZmdlBQDMzMzMzs0FAZmZmZmamQUDXo3A9CpdBQAAAAAAAoEFACtejcD2qQUBxPQrXo7BBQM3MzMzMrEFAPQrXo3CdQUAzMzMzM3NBQEjhehSuZ0FAFK5H4Xp0QUApXI/C9YhBQEjhehSuh0FAmpmZmZlZQUDsUbgehStBQPYoXI/CFUFA16NwPQoXQUBI4XoUridBQEjhehSuR0FAhetRuB5lQUCamZmZmXlBQEjhehSuh0FA4XoUrkeBQUBI4XoUrodBQEjhehSuh0FASOF6FK6HQUBI4XoUrodBQD0K16NwfUFAhetRuB5lQUBSuB6F61FBQHE9CtejUEFAMzMzMzNTQUBcj8L1KFxBQD0K16NwXUFAhetRuB5lQUA9CtejcF1BQB+F61G4XkFAj8L1KFxvQUDD9Shcj4JBQArXo3A9ikFAhetRuB6FQUCuR+F6FI5BQFyPwvUonEFA16NwPQqXQUCPwvUoXI9BQD0K16NwfUFACtejcD1qQUCF61G4HmVBQPYoXI/CVUFA7FG4HoVLQUAK16NwPUpBQHE9CtejMEFAMzMzMzMTQUCF61G4HiVBQPYoXI/CNUFA9ihcj8I1QUAfhetRuD5BQEjhehSuR0FAZmZmZmZGQUB7FK5H4TpBQFK4HoXrMUFAhetRuB4lQUC4HoXrURhBQM3MzMzMDEFAcT0K16MQQUDXo3A9ChdBQB+F61G4HkFA4XoUrkchQUBmZmZmZiZBQKRwPQrXI0FAw/UoXI8iQUBmZmZmZiZBQFyPwvUoHEFA7FG4HoULQUC4HoXrUfhAQHE9Ctej8EBACtejcD3qQEBmZmZmZuZAQK5H4XoU7kBAXI/C9Sj8QEA9CtejcP1AQOF6FK5HAUFAZmZmZmYGQUBI4XoUrgdBQOxRuB6FC0FAZmZmZmYGQUDD9ShcjwJBQNejcD0K90BAKVyPwvXoQEAUrkfhetRAQK5H4XoUzkBA16NwPQrXQEBmZmZmZuZAQFK4HoXr8UBACtejcD3qQEC4HoXrUdhAQDMzMzMz00BAFK5H4XrUQEBcj8L1KNxAQNejcD0K10BAcT0K16PQQEBI4XoUrsdAQFyPwvUovEBA9ihcj8K1QECPwvUoXK9AQOxRuB6Fq0BAhetRuB6lQEBI4XoUrqdAQFK4HoXrsUBAUrgeheuxQEAUrkfherRAQHE9CtejsEBArkfhehSuQEApXI/C9ahAQEjhehSup0BAUrgeheuxQEDXo3A9CrdAQLgehetRuEBAAAAAAADAQEDNzMzMzMxAQFyPwvUo3EBA4XoUrkfhQECF61G4HuVAQOxRuB6F60BA9ihcj8L1QEDXo3A9CvdAQOxRuB6F60BA16NwPQrXQEDsUbgehctAQIXrUbgexUBApHA9CtfDQEB7FK5H4bpAQOxRuB6Fq0BA4XoUrkehQEA9CtejcJ1AQLgehetRmEBAFK5H4XqUQECuR+F6FI5AQIXrUbgehUBAPQrXo3B9QEBcj8L1KHxAQJqZmZmZeUBAuB6F61F4QED2KFyPwnVAQLgehetReEBAmpmZmZl5QEBcj8L1KHxAQJqZmZmZeUBAmpmZmZl5QEDXo3A9CndAQNejcD0Kd0BAMzMzMzNzQEAUrkfhenRAQDMzMzMzc0BAj8L1KFxvQEDsUbgehWtAQKRwPQrXY0BAhetRuB5lQEBxPQrXo3BAQJqZmZmZeUBAXI/C9Sh8QEB7FK5H4XpAQOF6FK5HgUBA7FG4HoWLQEBmZmZmZoZAQAAAAAAAgEBAw/UoXI+CQECF61G4HoVAQFyPwvUofEBA9ihcj8J1QEAUrkfhenRAQHsUrkfhekBAXI/C9Sh8QECamZmZmXlAQHsUrkfhekBAcT0K16NwQECPwvUoXG9AQM3MzMzMbEBAzczMzMxsQECuR+F6FG5AQOxRuB6Fa0BA4XoUrkdhQECamZmZmVlAQPYoXI/CVUBAMzMzMzNTQEApXI/C9UhAQD0K16NwPUBAcT0K16MwQEAK16NwPSpAQIXrUbgeJUBA4XoUrkchQEDD9ShcjyJAQB+F61G4HkBAmpmZmZkZQEAUrkfhehRAQI/C9ShcD0BAZmZmZmYGQEA9CtejcP0/QHE9Ctej8D9ApHA9CtfjP0AfhetRuN4/QOF6FK5H4T9ArkfhehTuP0B7FK5H4fo/QD0K16Nw/T9AuB6F61H4P0CuR+F6FO4/QK5H4XoU7j9AKVyPwvXoP0Bcj8L1KNw/QFyPwvUo3D9A4XoUrkfhP0CkcD0K1+M/QKRwPQrX4z9APQrXo3D9P0DhehSuRwFAQD0K16Nw/T9A9ihcj8L1P0DhehSuR+E/QBSuR+F61D9ACtejcD3KP0AAAAAAAMA/QLgehetRuD9AuB6F61G4P0D2KFyPwrU/QLgehetRuD9AMzMzMzOzP0ApXI/C9ag/QOF6FK5HoT9A16NwPQqXP0AfhetRuJ4/QOxRuB6Fqz9ArkfhehSuP0Bcj8L1KJw/QEjhehSuhz9A9ihcj8J1P0AUrkfhelQ/QD0K16NwPT9A16NwPQoXP0BI4XoUrgc/QD0K16Nw/T5A7FG4HoXrPkCkcD0K1+M+QBSuR+F61D5AAAAAAADAPkBxPQrXo7A+QOF6FK5HoT5AmpmZmZmZPkCPwvUoXI8+QLgehetReD5AKVyPwvVoPkAfhetRuF4+QFK4HoXrUT5AFK5H4XpUPkAUrkfhelQ+QI/C9ShcTz5AhetRuB5FPkDD9Shcj0I+QDMzMzMzMz5A7FG4HoUrPkCuR+F6FC4+QB+F61G4Hj5AmpmZmZkZPkDXo3A9Chc+QArXo3A9Cj5APQrXo3D9PUD2KFyPwvU9QHE9Ctej8D1A7FG4HoXrPUAzMzMzM/M9QKRwPQrX4z1AmpmZmZnZPUCPwvUoXM89QEjhehSuxz1Aw/UoXI/CPUDD9Shcj8I9QHE9CtejsD1ArkfhehSuPUBmZmZmZqY9QOF6FK5HoT1AXI/C9SicPUAUrkfhepQ9QArXo3A9ij1ASOF6FK6HPUAAAAAAAIA9QLgehetReD1A7FG4HoVrPUCkcD0K12M9QOF6FK5HYT1A4XoUrkdhPUDhehSuR2E9QNejcD0KVz1ASOF6FK5HPUC4HoXrUTg9QGZmZmZmJj1AmpmZmZkZPUDNzMzMzAw9QHsUrkfh+jxArkfhehTuPEBcj8L1KNw8QArXo3A9yjxAw/UoXI/CPEA9CtejcL08QDMzMzMzszxA7FG4HoWrPECkcD0K16M8QOF6FK5HoTxApHA9CtejPEDhehSuR6E8QB+F61G4njxAmpmZmZmZPEAUrkfhepQ8QArXo3A9ijxAw/UoXI+CPEC4HoXrUXg8QOxRuB6FazxApHA9CtdjPECamZmZmVk8QJqZmZmZWTxAFK5H4XpUPEAK16NwPUo8QAAAAAAAQDxAuB6F61E4PEBmZmZmZiY8QB+F61G4HjxAXI/C9SgcPEBSuB6F6xE8QEjhehSuBzxAAAAAAAAAPEB7FK5H4fo7QLgehetR+DtArkfhehTuO0DhehSuR+E7QB+F61G43jtA16NwPQrXO0DNzMzMzMw7QIXrUbgexTtAexSuR+G6O0CuR+F6FK47QClcj8L1qDtApHA9CtejO0AfhetRuJ47QNejcD0KlztAFK5H4XqUO0BSuB6F65E7QI/C9ShcjztAUrgeheuRO0DNzMzMzIw7QI/C9ShcjztACtejcD2KO0BI4XoUroc7QMP1KFyPgjtAAAAAAACAO0A9CtejcH07QAAAAAAAgDtAexSuR+F6O0D2KFyPwnU7QK5H4XoUbjtAKVyPwvVoO0DhehSuR2E7QKRwPQrXYztAUrgehetRO0A9CtejcD07QDMzMzMzMztAPQrXo3A9O0AAAAAAAEA7QMP1KFyPQjtA9ihcj8I1O0BxPQrXozA7QHE9CtejMDtArkfhehQuO0CuR+F6FC47QDMzMzMzMztAcT0K16MwO0B7FK5H4To7QMP1KFyPQjtASOF6FK5HO0CF61G4HkU7QIXrUbgeRTtAhetRuB5FO0BI4XoUrkc7QM3MzMzMTDtAhetRuB5FO0A9CtejcD07QLgehetRODtAMzMzMzMzO0BmZmZmZiY7QFyPwvUoHDtAFK5H4XoUO0DNzMzMzAw7QMP1KFyPAjtAuB6F61H4OkCuR+F6FO46QGZmZmZm5jpAmpmZmZnZOkBSuB6F69E6QArXo3A9yjpAw/UoXI/COkB7FK5H4bo6QDMzMzMzszpArkfhehSuOkDhehSuR6E6QBSuR+F6lDpACtejcD2KOkCF61G4HoU6QAAAAAAAgDpAexSuR+F6OkB7FK5H4Xo6QD0K16NwfTpAuB6F61F4OkCuR+F6FG46QClcj8L1aDpAZmZmZmZmOkBmZmZmZmY6QGZmZmZmZjpApHA9CtdjOkBcj8L1KFw6QFK4HoXrUTpAUrgehetROkCPwvUoXE86QM3MzMzMTDpASOF6FK5HOkAAAAAAAEA6QPYoXI/CNTpArkfhehQuOkAzMzMzMzM6QHE9CtejMDpApHA9CtcjOkBSuB6F6xE6QArXo3A9CjpAw/UoXI8COkDD9ShcjwI6QMP1KFyPAjpAAAAAAAAAOkAAAAAAAAA6QPYoXI/C9TlAKVyPwvXoOUCkcD0K1+M5QB+F61G43jlAH4XrUbjeOUCamZmZmdk5QJqZmZmZ2TlA4XoUrkfhOUCkcD0K1+M5QOF6FK5H4TlAKVyPwvXoOUApXI/C9eg5QKRwPQrX4zlApHA9CtfjOUAfhetRuN45QJqZmZmZ2TlAmpmZmZnZOUDXo3A9Ctc5QFyPwvUo3DlAmpmZmZnZOUBcj8L1KNw5QJqZmZmZ2TlAmpmZmZnZOUCamZmZmdk5QBSuR+F61DlAzczMzMzMOUAK16NwPco5QIXrUbgexTlAw/UoXI/COUDD9Shcj8I5QMP1KFyPwjlAPQrXo3C9OUB7FK5H4bo5QLgehetRuDlAuB6F61G4OUD2KFyPwrU5QHE9CtejsDlA7FG4HoWrOUBmZmZmZqY5QOF6FK5HoTlAmpmZmZmZOUAUrkfhepQ5QI/C9ShcjzlAzczMzMyMOUBI4XoUroc5QIXrUbgehTlAAAAAAACAOUD2KFyPwnU5QK5H4XoUbjlApHA9CtdjOUAfhetRuF45QJqZmZmZWTlAXI/C9ShcOUCamZmZmVk5QFK4HoXrUTlAUrgehetROUCPwvUoXE85QArXo3A9SjlASOF6FK5HOUA9CtejcD05QMP1KFyPQjlAzczMzMxMOUAUrkfhelQ5QFyPwvUoXDlAZmZmZmZmOUAzMzMzM3M5QD0K16NwfTlASOF6FK6HOUBI4XoUroc5QEjhehSuhzlASOF6FK6HOUDD9Shcj4I5QHsUrkfhejlAcT0K16NwOUBmZmZmZmY5QB+F61G4XjlAj8L1KFxPOUDD9Shcj0I5QHE9CtejMDlAMzMzMzMzOUDD9Shcj0I5QBSuR+F6VDlAXI/C9ShcOUAfhetRuF45QArXo3A9SjlAuB6F61E4OUApXI/C9Sg5QBSuR+F6FDlASOF6FK4HOUC4HoXrUfg4QGZmZmZm5jhAFK5H4XrUOEAAAAAAAMA4QD0K16NwvThAexSuR+G6OEBI4XoUrsc4QNejcD0K1zhAUrgehevROEDD9Shcj8I4QK5H4XoUrjhAKVyPwvWoOEBcj8L1KJw4QEjhehSuhzhAPQrXo3B9OED2KFyPwnU4QK5H4XoUbjhAFK5H4XpUOEB7FK5H4To4QClcj8L1KDhAXI/C9SgcOECPwvUoXA84QMP1KFyPAjhAw/UoXI8COEDD9ShcjwI4QHE9Ctej8DdAZmZmZmbmN0AUrkfhetQ3QFK4HoXr0TdAj8L1KFzPN0BSuB6F69E3QEjhehSuxzdAAAAAAADAN0C4HoXrUbg3QPYoXI/CtTdAKVyPwvWoN0CamZmZmZk3QBSuR+F6lDdA16NwPQqXN0CamZmZmZk3QB+F61G4njdArkfhehSuN0DsUbgehas3QJqZmZmZmTdAFK5H4XqUN0DNzMzMzIw3QAAAAAAAgDdAuB6F61F4N0BxPQrXo3A3QClcj8L1aDdA4XoUrkdhN0DhehSuR2E3QOF6FK5HYTdAmpmZmZlZN0CPwvUoXE83QIXrUbgeRTdAAAAAAABAN0A9CtejcD03QHsUrkfhOjdAuB6F61E4N0D2KFyPwjU3QDMzMzMzMzdAMzMzMzMzN0AzMzMzMzM3QLgehetRODdAexSuR+E6N0A9CtejcD03QD0K16NwPTdA9ihcj8I1N0DsUbgehSs3QGZmZmZmJjdApHA9CtcjN0CamZmZmRk3QFK4HoXrETdAj8L1KFwPN0AK16NwPQo3QIXrUbgeBTdAPQrXo3D9NkAzMzMzM/M2QClcj8L16DZAXI/C9SjcNkAUrkfhetQ2QI/C9ShczzZAzczMzMzMNkAK16NwPco2QIXrUbgexTZACtejcD3KNkAK16NwPco2QI/C9ShczzZAzczMzMzMNkBI4XoUrsc2QI/C9ShczzZAXI/C9SjcNkDsUbgehes2QD0K16Nw/TZAw/UoXI8CN0CF61G4HgU3QMP1KFyPAjdAPQrXo3D9NkAzMzMzM/M2QGZmZmZm5jZAH4XrUbjeNkDXo3A9Ctc2QI/C9ShczzZAhetRuB7FNkC4HoXrUbg2QHE9CtejsDZApHA9CtejNkCamZmZmZk2QI/C9ShcjzZASOF6FK6HNkA9CtejcH02QK5H4XoUbjZAKVyPwvVoNkDhehSuR2E2QOF6FK5HYTZApHA9CtdjNkBcj8L1KFw2QJqZmZmZWTZAFK5H4XpUNkCPwvUoXE82QM3MzMzMTDZASOF6FK5HNkC4HoXrUTg2QDMzMzMzMzZArkfhehQuNkBxPQrXozA2QPYoXI/CNTZAAAAAAABANkAK16NwPUo2QI/C9ShcTzZAH4XrUbheNkDhehSuR2E2QGZmZmZmZjZAKVyPwvVoNkBxPQrXo3A2QD0K16NwfTZAw/UoXI+CNkCF61G4HoU2QI/C9ShcjzZApHA9CtejNkApXI/C9ag2QOxRuB6FqzZAuB6F61G4NkB7FK5H4bo2QHE9CtejsDZA7FG4HoWrNkCuR+F6FK42QDMzMzMzszZAPQrXo3C9NkDD9Shcj8I2QHsUrkfhujZAMzMzMzOzNkAzMzMzM7M2QK5H4XoUrjZAKVyPwvWoNkBcj8L1KJw2QJqZmZmZmTZApHA9CtejNkBmZmZmZqY2QKRwPQrXozZA7FG4HoWrNkAAAAAAAMA2QI/C9ShczzZAH4XrUbjeNkCuR+F6FO42QHE9Ctej8DZA9ihcj8L1NkBxPQrXo/A2QClcj8L16DZAXI/C9SjcNkBSuB6F69E2QIXrUbgexTZAMzMzMzOzNkCuR+F6FK42QOxRuB6FqzZAKVyPwvWoNkCkcD0K16M2QB+F61G4njZAXI/C9SicNkAUrkfhepQ2QArXo3A9ijZAKVyPwvVoNkAzMzMzMzM2QArXo3A9CjZAcT0K16PwNUBI4XoUrsc1QJqZmZmZmTVAhetRuB6FNUD2KFyPwnU1QGZmZmZmZjVAUrgehetRNUAK16NwPUo1QEjhehSuRzVAAAAAAABANUD2KFyPwjU1QDMzMzMzMzVAw/UoXI9CNUBI4XoUrkc1QMP1KFyPQjVA9ihcj8I1NUBxPQrXozA1QOxRuB6FKzVArkfhehQuNUBxPQrXozA1QHE9CtejMDVAMzMzMzMzNUBxPQrXozA1QGZmZmZmJjVAXI/C9SgcNUAUrkfhehQ1QI/C9ShcDzVAFK5H4XoUNUDhehSuRyE1QClcj8L1KDVArkfhehQuNUCuR+F6FC41QClcj8L1KDVAH4XrUbgeNUDXo3A9Chc1QB+F61G4HjVAUrgehesRNUDNzMzMzAw1QJqZmZmZGTVAUrgehesRNUAK16NwPQo1QArXo3A9CjVASOF6FK4HNUAAAAAAAAA1QDMzMzMz8zRA9ihcj8L1NEB7FK5H4fo0QAAAAAAAADVAAAAAAAAANUD2KFyPwvU0QDMzMzMz8zRAcT0K16PwNECkcD0K1+M0QNejcD0K1zRAzczMzMzMNECF61G4HsU0QAAAAAAAwDRAPQrXo3C9NEA9CtejcL00QLgehetRuDRAuB6F61G4NEAzMzMzM7M0QK5H4XoUrjRApHA9CtejNECPwvUoXI80QAAAAAAAgDRAcT0K16NwNEBmZmZmZmY0QB+F61G4XjRAzczMzMxMNEDD9Shcj0I0QLgehetRODRAuB6F61E4NEC4HoXrUTg0QDMzMzMzMzRA7FG4HoUrNECkcD0K1yM0QOF6FK5HITRAXI/C9SgcNEBcj8L1KBw0QJqZmZmZGTRAmpmZmZkZNECamZmZmRk0QJqZmZmZGTRAmpmZmZkZNEBcj8L1KBw0QJqZmZmZGTRAXI/C9SgcNEBcj8L1KBw0QB+F61G4HjRAXI/C9SgcNEDXo3A9Chc0QBSuR+F6FDRAmpmZmZkZNEApXI/C9Sg0QHE9CtejMDRAexSuR+E6NEDD9Shcj0I0QEjhehSuRzRASOF6FK5HNECF61G4HkU0QMP1KFyPQjRAAAAAAABANEDD9Shcj0I0QMP1KFyPQjRACtejcD1KNEBSuB6F61E0QBSuR+F6VDRA16NwPQpXNEAUrkfhelQ0QFK4HoXrUTRAFK5H4XpUNEAUrkfhelQ0QJqZmZmZWTRA16NwPQpXNECamZmZmVk0QFyPwvUoXDRAH4XrUbheNEDhehSuR2E0QGZmZmZmZjRA7FG4HoVrNEBxPQrXo3A0QPYoXI/CdTRAuB6F61F4NECF61G4HoU0QIXrUbgehTRAhetRuB6FNEAK16NwPYo0QNejcD0KlzRAXI/C9SicNECamZmZmZk0QOF6FK5HoTRApHA9CtejNEAzMzMzM7M0QArXo3A9yjRAFK5H4XrUNECkcD0K1+M0QK5H4XoU7jRAcT0K16PwNED2KFyPwvU0QD0K16Nw/TRAw/UoXI8CNUDNzMzMzAw1QFyPwvUoHDVAZmZmZmYmNUCuR+F6FC41QOxRuB6FKzVA7FG4HoUrNUDsUbgehSs1QHE9CtejMDVAPQrXo3A9NUCPwvUoXE81QKRwPQrXYzVA9ihcj8J1NUDD9Shcj4I1QI/C9ShcjzVAXI/C9SicNUBmZmZmZqY1QClcj8L1qDVArkfhehSuNUB7FK5H4bo1QEjhehSuxzVACtejcD3KNUCPwvUoXM81QJqZmZmZ2TVA7FG4HoXrNUC4HoXrUfg1QD0K16Nw/TVAzczMzMwMNkCamZmZmRk2QOxRuB6FKzZAMzMzMzMzNkB7FK5H4To2QMP1KFyPQjZAj8L1KFxPNkDhehSuR2E2QFyPwvUoXDZA4XoUrkdhNkApXI/C9Wg2QDMzMzMzczZAZmZmZmZmNkDsUbgehWs2QClcj8L1aDZAuB6F61F4NkB7FK5H4Xo2QK5H4XoUbjZAH4XrUbheNkAfhetRuF42QFyPwvUoXDZAFK5H4XpUNkDNzMzMzEw2QI/C9ShcTzZAUrgehetRNkAUrkfhelQ2QFyPwvUoXDZApHA9CtdjNkCuR+F6FG42QPYoXI/CdTZA9ihcj8J1NkB7FK5H4Xo2QEjhehSuhzZAFK5H4XqUNkCamZmZmZk2QB+F61G4njZApHA9CtejNkApXI/C9ag2QK5H4XoUrjZA9ihcj8K1NkA9CtejcL02QAAAAAAAwDZAhetRuB7FNkBI4XoUrsc2QM3MzMzMzDZACtejcD3KNkDNzMzMzMw2QFK4HoXr0TZAFK5H4XrUNkCamZmZmdk2QI/C9ShczzZAKVyPwvWoNkBmZmZmZqY2QClcj8L1qDZAcT0K16OwNkAUrkfhepQ2QMP1KFyPgjZAFK5H4XqUNkBmZmZmZqY2QHsUrkfhujZAj8L1KFzPNkCkcD0K1+M2QKRwPQrX4zZAuB6F61G4NkDNzMzMzIw2QDMzMzMzczZA7FG4HoVrNkCkcD0K12M2QClcj8L1aDZApHA9CtdjNkCuR+F6FG42QM3MzMzMjDZA16NwPQqXNkCamZmZmZk2QKRwPQrXozZA9ihcj8K1NkBI4XoUrsc2QJqZmZmZ2TZAZmZmZmbmNkB7FK5H4fo2QArXo3A9CjdAzczMzMwMN0AUrkfhehQ3QJqZmZmZGTdAmpmZmZkZN0Bcj8L1KBw3QGZmZmZmJjdA7FG4HoUrN0D2KFyPwjU3QD0K16NwPTdApHA9CtcjN0BSuB6F69E2QFyPwvUonDZAAAAAAACANkD2KFyPwnU2QHE9CtejcDZAZmZmZmZmNkDNzMzMzEw2QD0K16NwPTZA7FG4HoUrNkAK16NwPQo2QOxRuB6F6zVAcT0K16PwNUApXI/C9eg1QBSuR+F61DVAzczMzMzMNUBSuB6F69E1QHsUrkfhujVAKVyPwvWoNUDhehSuR6E1QClcj8L1qDVAKVyPwvWoNUD2KFyPwrU1QI/C9ShczzVAMzMzMzPzNUCPwvUoXA82QOF6FK5HITZAMzMzMzMzNkAUrkfhelQ2QAAAAAAAgDZAH4XrUbieNkC4HoXrUbg2QHE9CtejsDZA4XoUrkehNkD2KFyPwrU2QM3MzMzMzDZA7FG4HoXrNkC4HoXrUfg2QMP1KFyPAjdA16NwPQoXN0CuR+F6FC43QAAAAAAAQDdA16NwPQpXN0BxPQrXo3A3QIXrUbgehTdAXI/C9SicN0DhehSuR6E3QKRwPQrXozdAPQrXo3C9N0AfhetRuN43QPYoXI/C9TdAexSuR+H6N0CF61G4HgU4QJqZmZmZGThAZmZmZmYmOECuR+F6FC44QLgehetRODhASOF6FK5HOEDhehSuR2E4QIXrUbgehThAzczMzMyMOEBI4XoUroc4QI/C9ShcjzhApHA9CtejOECuR+F6FK44QD0K16NwvThAFK5H4XrUOEBSuB6F69E4QNejcD0K1zhAZmZmZmbmOEDhehSuR+E4QFK4HoXr0ThAKVyPwvXoOEAK16NwPQo5QKRwPQrXIzlAmpmZmZkZOUA9CtejcP04QLgehetR+DhASOF6FK4HOUBcj8L1KBw5QDMzMzMzMzlAexSuR+E6OUAzMzMzMzM5QAAAAAAAQDlAPQrXo3A9OUAAAAAAAEA5QMP1KFyPQjlAhetRuB5FOUDXo3A9Clc5QK5H4XoUbjlA9ihcj8J1OUCkcD0K12M5QEjhehSuRzlArkfhehQuOUBxPQrXozA5QPYoXI/CNTlAPQrXo3A9OUAAAAAAAEA5QOxRuB6FKzlAmpmZmZkZOUDhehSuRyE5QDMzMzMzMzlAPQrXo3A9OUD2KFyPwjU5QDMzMzMzMzlAw/UoXI9COUBcj8L1KFw5QK5H4XoUbjlApHA9CtdjOUCamZmZmVk5QJqZmZmZWTlAH4XrUbheOUAUrkfhelQ5QKRwPQrXYzlAhetRuB6FOUApXI/C9ag5QPYoXI/CtTlAPQrXo3C9OUCPwvUoXM85QOF6FK5H4TlAH4XrUbjeOUCkcD0K1+M5QKRwPQrX4zlAMzMzMzPzOUDNzMzMzAw6QMP1KFyPAjpAAAAAAAAAOkCF61G4HgU6QM3MzMzMDDpAZmZmZmYmOkCuR+F6FC46QDMzMzMzMzpAcT0K16MwOkAfhetRuB46QNejcD0KFzpAFK5H4XoUOkDXo3A9Chc6QJqZmZmZGTpAUrgehesROkCPwvUoXA86QOF6FK5HITpASOF6FK5HOkCuR+F6FG46QFyPwvUonDpACtejcD3KOkBxPQrXo/A6QLgehetR+DpAmpmZmZnZOkApXI/C9ag6QClcj8L1qDpAAAAAAADAOkCamZmZmdk6QAAAAAAAADtAUrgehesRO0CuR+F6FC47QJqZmZmZGTtAuB6F61H4OkCamZmZmdk6QFyPwvUo3DpAUrgehevROkAfhetRuN46QEjhehSuBztApHA9CtcjO0BmZmZmZiY7QGZmZmZmJjtA7FG4HoUrO0CF61G4HkU7QD0K16NwfTtArkfhehSuO0CuR+F6FK47QOF6FK5HoTtA7FG4HoVrO0CPwvUoXE87QNejcD0KVztAH4XrUbheO0CkcD0K12M7QK5H4XoUbjtAMzMzMzNzO0CamZmZmVk7QB+F61G4XjtA7FG4HoVrO0AK16NwPYo7QI/C9ShcjztAUrgeheuRO0BmZmZmZqY7QI/C9ShczztA7FG4HoXrO0CuR+F6FO47QOxRuB6F6ztAMzMzMzPzO0AK16NwPQo8QHsUrkfh+jtAuB6F61H4O0DNzMzMzAw8QOxRuB6F6ztAj8L1KFzPO0DsUbgehas7QPYoXI/CtTtAuB6F61H4O0BI4XoUrgc8QIXrUbgeBTxAhetRuB4FPEDsUbgehSs8QM3MzMzMTDxArkfhehQuPEAfhetRuB48QD0K16NwPTxApHA9CtdjPEDXo3A9Cpc8QNejcD0KlzxACtejcD2KPEDXo3A9Cpc8QEjhehSuxzxAAAAAAADAPEBxPQrXo7A8QClcj8L1qDxAzczMzMyMPEApXI/C9Wg8QI/C9ShcTzxAhetRuB5FPEBI4XoUrkc8QArXo3A9SjxArkfhehRuPEAK16NwPYo8QB+F61G4njxAUrgehevRPEC4HoXrUbg8QHE9CtejcDxAhetRuB6FPEBxPQrXo3A8QFK4HoXrUTxA9ihcj8J1PEBSuB6F65E8QFK4HoXrkTxArkfhehSuPECPwvUoXM88QPYoXI/CtTxAFK5H4XrUPEC4HoXrUfg8QBSuR+F61DxAw/UoXI/CPEBmZmZmZuY8QAAAAAAAwDxAXI/C9SicPEBI4XoUrsc8QOxRuB6F6zxApHA9CtfjPEDD9ShcjwI9QFK4HoXr0TxA9ihcj8K1PEBI4XoUrsc8QClcj8L16DxAFK5H4XoUPUBcj8L1KBw9QFyPwvUoXD1ACtejcD1KPUBI4XoUrgc9QD0K16Nw/TxAw/UoXI8CPUAAAAAAAMA8QD0K16NwvTxAPQrXo3C9PEBcj8L1KNw8QI/C9ShczzxAcT0K16OwPEAzMzMzM7M8QMP1KFyPAj1AuB6F61E4PUDD9Shcj0I9QHsUrkfhOj1AZmZmZmYmPUAAAAAAAEA9QD0K16NwPT1AzczMzMxMPUCuR+F6FC49QGZmZmZmJj1APQrXo3A9PUDsUbgehSs9QClcj8L16DxAKVyPwvXoPEBcj8L1KBw9QEjhehSuRz1AMzMzMzNzPUAK16NwPYo9QBSuR+F6lD1AH4XrUbiePUAUrkfhetQ9QNejcD0KFz5AXI/C9SgcPkAK16NwPQo+QArXo3A9Cj5AFK5H4XoUPkDNzMzMzAw+QIXrUbgeBT5AzczMzMwMPkBI4XoUrgc+QAAAAAAAAD5A9ihcj8L1PUCamZmZmdk9QClcj8L1qD1AexSuR+G6PUAUrkfhetQ9QPYoXI/C9T1Aj8L1KFwPPkB7FK5H4To+QIXrUbgeRT5ASOF6FK5HPkCF61G4HkU+QOxRuB6Faz5AZmZmZmZmPkDXo3A9Clc+QArXo3A9Sj5A4XoUrkchPkDNzMzMzAw+QHsUrkfh+j1AmpmZmZnZPUCkcD0K1+M9QPYoXI/CNT5A9ihcj8J1PkAUrkfhepQ+QFK4HoXrkT5A4XoUrkehPkB7FK5H4bo+QK5H4XoUrj5ACtejcD2KPkCuR+F6FK4+QOxRuB6Fqz5AXI/C9SicPkAK16NwPUo+QFyPwvUo3D1AXI/C9SgcPkD2KFyPwjU+QFK4HoXrUT5AmpmZmZlZPkAfhetRuB4+QLgehetROD5APQrXo3A9PkBmZmZmZmY+QMP1KFyPwj5AMzMzMzPzPkD2KFyPwvU+QK5H4XoU7j5ArkfhehSuPkCkcD0K12M+QGZmZmZmJj5AMzMzMzMzPkAUrkfhelQ+QNejcD0KVz5AZmZmZmZmPkA9CtejcH0+QEjhehSuhz5Aj8L1KFyPPkAUrkfhepQ+QFK4HoXrkT5AH4XrUbiePkCF61G4HsU+QM3MzMzMzD5AFK5H4XqUPkCPwvUoXE8+QClcj8L1aD5A9ihcj8J1PkCuR+F6FG4+QM3MzMzMjD5AhetRuB7FPkApXI/C9eg+QPYoXI/C9T5AZmZmZmbmPkAzMzMzM7M+QFK4HoXrkT5Aj8L1KFyPPkCamZmZmZk+QEjhehSuhz5AmpmZmZmZPkAzMzMzM7M+QNejcD0K1z5AAAAAAAAAP0AUrkfhetQ+QI/C9Shczz5ApHA9CtfjPkAzMzMzM/M+QArXo3A9Cj9AexSuR+E6P0AfhetRuF4/QFK4HoXrUT9AZmZmZmYmP0CamZmZmRk/QB+F61G4Hj9AzczMzMwMP0Bcj8L1KBw/QHsUrkfhOj9A16NwPQpXP0CPwvUoXE8/QDMzMzMzMz9AZmZmZmYmP0A9CtejcD0/QHE9CtejMD9Aw/UoXI8CP0AzMzMzM/M+QEjhehSuBz9Aj8L1KFwPP0DhehSuRyE/QArXo3A9Sj9AKVyPwvUoP0C4HoXrUfg+QGZmZmZm5j5ApHA9CtfjPkDhehSuR+E+QBSuR+F61D5A9ihcj8L1PkBcj8L1KBw/QDMzMzMzMz9ASOF6FK5HP0CF61G4HkU/QFyPwvUoXD9Aj8L1KFyPP0Bcj8L1KJw/QAAAAAAAgD9APQrXo3A9P0CkcD0K1yM/QGZmZmZmJj9AzczMzMwMP0BI4XoUrgc/QD0K16Nw/T5Aj8L1KFwPP0AfhetRuB4/QHE9Ctej8D5ASOF6FK4HP0B7FK5H4fo+QIXrUbgeBT9AKVyPwvUoP0ApXI/C9Sg/QClcj8L1KD9A16NwPQp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\",\"dtype\":\"float64\",\"shape\":[5760]},\"GHI\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[5760]}},\"selected\":{\"id\":\"3389\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"3390\",\"type\":\"UnionRenderers\"}},\"id\":\"3309\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"3317\",\"type\":\"LinearScale\"},{\"attributes\":{\"formatter\":{\"id\":\"3348\",\"type\":\"DatetimeTickFormatter\"},\"ticker\":{\"id\":\"3320\",\"type\":\"DatetimeTicker\"}},\"id\":\"3319\",\"type\":\"DatetimeAxis\"},{\"attributes\":{\"source\":{\"id\":\"3309\",\"type\":\"ColumnDataSource\"}},\"id\":\"3508\",\"type\":\"CDSView\"},{\"attributes\":{\"text\":\"\"},\"id\":\"3510\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"3512\",\"type\":\"DatetimeTickFormatter\"},{\"attributes\":{},\"id\":\"3329\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"3514\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"mantissas\":[1,2,5],\"max_interval\":500.0,\"num_minor_ticks\":0},\"id\":\"3515\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"max_interval\":1800000.0,\"min_interval\":1000.0,\"num_minor_ticks\":0},\"id\":\"3516\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"max_interval\":43200000.0,\"min_interval\":3600000.0,\"num_minor_ticks\":0},\"id\":\"3517\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]},\"id\":\"3518\",\"type\":\"DaysTicker\"},{\"attributes\":{\"days\":[1,4,7,10,13,16,19,22,25,28]},\"id\":\"3519\",\"type\":\"DaysTicker\"},{\"attributes\":{\"months\":[0,4,8]},\"id\":\"3360\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"3520\",\"type\":\"DaysTicker\"},{\"attributes\":{\"days\":[1,15]},\"id\":\"3521\",\"type\":\"DaysTicker\"},{\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]},\"id\":\"3522\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"months\":[0,2,4,6,8,10]},\"id\":\"3523\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"months\":[0,4,8]},\"id\":\"3524\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"months\":[0,6]},\"id\":\"3525\",\"type\":\"MonthsTicker\"},{\"attributes\":{},\"id\":\"3526\",\"type\":\"YearsTicker\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"3527\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"click_policy\":\"hide\",\"items\":[{\"id\":\"3529\",\"type\":\"LegendItem\"}],\"location\":\"top_left\"},\"id\":\"3528\",\"type\":\"Legend\"},{\"attributes\":{\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"GHI\"}},\"id\":\"3341\",\"type\":\"Line\"},{\"attributes\":{\"label\":{\"value\":\"Wind speed\"},\"renderers\":[{\"id\":\"3507\",\"type\":\"GlyphRenderer\"}]},\"id\":\"3529\",\"type\":\"LegendItem\"},{\"attributes\":{\"tools\":[{\"id\":\"3329\",\"type\":\"PanTool\"},{\"id\":\"3330\",\"type\":\"BoxZoomTool\"},{\"id\":\"3331\",\"type\":\"WheelZoomTool\"},{\"id\":\"3332\",\"type\":\"ResetTool\"},{\"id\":\"3333\",\"type\":\"SaveTool\"},{\"id\":\"3438\",\"type\":\"PanTool\"},{\"id\":\"3439\",\"type\":\"BoxZoomTool\"},{\"id\":\"3440\",\"type\":\"WheelZoomTool\"},{\"id\":\"3441\",\"type\":\"ResetTool\"},{\"id\":\"3442\",\"type\":\"SaveTool\"},{\"id\":\"3493\",\"type\":\"PanTool\"},{\"id\":\"3494\",\"type\":\"BoxZoomTool\"},{\"id\":\"3495\",\"type\":\"WheelZoomTool\"},{\"id\":\"3496\",\"type\":\"ResetTool\"},{\"id\":\"3497\",\"type\":\"SaveTool\"}]},\"id\":\"3597\",\"type\":\"ProxyToolbar\"},{\"attributes\":{\"toolbar\":{\"id\":\"3597\",\"type\":\"ProxyToolbar\"},\"toolbar_location\":\"above\"},\"id\":\"3598\",\"type\":\"ToolbarBox\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"3325\",\"type\":\"BasicTicker\"}},\"id\":\"3328\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"3390\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"callback\":null,\"formatters\":{\"index\":\"datetime\"},\"mode\":\"vline\",\"tooltips\":[[\"timestamp\",\"@index{%FT%H:%M:%S%z}\"],[\"GHI\",\"@GHI\"],[\"DNI\",\"@DNI\"],[\"DHI\",\"@DHI\"]]},\"id\":\"3600\",\"type\":\"HoverTool\"},{\"attributes\":{\"overlay\":{\"id\":\"3363\",\"type\":\"BoxAnnotation\"}},\"id\":\"3330\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"3325\",\"type\":\"BasicTicker\"},{\"attributes\":{\"callback\":null},\"id\":\"3313\",\"type\":\"DataRange1d\"}],\"root_ids\":[\"3599\"]},\"title\":\"Bokeh Application\",\"version\":\"1.3.4\"}};\n",
       "  var render_items = [{\"docid\":\"7efdff39-7622-42e9-9cbb-f3aa7cdcc572\",\"roots\":{\"3599\":\"61ee467c-2bf6-4bf8-8912-ad750983cebb\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "3599"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = plot_nwp(*[oasis[v] for v in plot_vars], ac_power)\n",
    "show(grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sunrise around 12:30."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "oasis_1h_right = oasis.resample('1h', label='right', closed='right').mean()\n",
    "oasis_1h_left = oasis.resample('1h', label='left', closed='left').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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temperature\"}},\"id\":\"4337\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"4325\",\"type\":\"PanTool\"},{\"attributes\":{\"overlay\":{\"id\":\"4359\",\"type\":\"BoxAnnotation\"}},\"id\":\"4326\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]},\"id\":\"4241\",\"type\":\"DaysTicker\"},{\"attributes\":{\"dimensions\":\"width\"},\"id\":\"4327\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"max_interval\":43200000.0,\"min_interval\":3600000.0,\"num_minor_ticks\":0},\"id\":\"4240\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{},\"id\":\"4328\",\"type\":\"ResetTool\"},{\"attributes\":{\"data_source\":{\"id\":\"4196\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"4337\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"4338\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"4340\",\"type\":\"CDSView\"}},\"id\":\"4339\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"source\":{\"id\":\"4196\",\"type\":\"ColumnDataSource\"}},\"id\":\"4340\",\"type\":\"CDSView\"},{\"attributes\":{\"text\":\"\"},\"id\":\"4342\",\"type\":\"Title\"},{\"attributes\":{\"days\":[1,4,7,10,13,16,19,22,25,28]},\"id\":\"4242\",\"type\":\"DaysTicker\"},{\"attributes\":{},\"id\":\"4344\",\"type\":\"DatetimeTickFormatter\"},{\"attributes\":{\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"max_interval\":1800000.0,\"min_interval\":1000.0,\"num_minor_ticks\":0},\"id\":\"4239\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{},\"id\":\"4346\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"4237\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"mantissas\":[1,2,5],\"max_interval\":500.0,\"num_minor_ticks\":0},\"id\":\"4347\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"max_interval\":1800000.0,\"min_interval\":1000.0,\"num_minor_ticks\":0},\"id\":\"4348\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"max_interval\":43200000.0,\"min_interval\":3600000.0,\"num_minor_ticks\":0},\"id\":\"4349\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]},\"id\":\"4350\",\"type\":\"DaysTicker\"},{\"attributes\":{\"days\":[1,4,7,10,13,16,19,22,25,28]},\"id\":\"4351\",\"type\":\"DaysTicker\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"4352\",\"type\":\"DaysTicker\"},{\"attributes\":{},\"id\":\"4235\",\"type\":\"DatetimeTickFormatter\"},{\"attributes\":{\"days\":[1,15]},\"id\":\"4353\",\"type\":\"DaysTicker\"},{\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]},\"id\":\"4354\",\"type\":\"MonthsTicker\"}],\"root_ids\":[\"4486\"]},\"title\":\"Bokeh Application\",\"version\":\"1.3.4\"}};\n",
       "  var render_items = [{\"docid\":\"2d9f2b67-f85e-4658-a7f9-2a88e7f1ae4a\",\"roots\":{\"4486\":\"883086fe-34f5-48d2-879f-a3926e1ce482\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "4486"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = plot_nwp(*[oasis_1h_right[v] for v in plot_vars], ac_power)\n",
    "show(grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"0cd20b5f-abfc-466a-ba88-fc0ab3fcc992\" data-root-id=\"5432\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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Application\",\"version\":\"1.3.4\"}};\n",
       "  var render_items = [{\"docid\":\"9b848193-a6eb-4298-9e2f-b49ad735c6db\",\"roots\":{\"5432\":\"0cd20b5f-abfc-466a-ba88-fc0ab3fcc992\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "5432"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = plot_nwp(*[oasis_1h_left[v] for v in plot_vars], ac_power)\n",
    "show(grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = reload(models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time\n",
      "2019-05-15 02:00:00+00:00     70.906209\n",
      "2019-05-15 03:00:00+00:00      0.115150\n",
      "2019-05-15 04:00:00+00:00      0.000000\n",
      "2019-05-15 05:00:00+00:00      0.000000\n",
      "2019-05-15 06:00:00+00:00      0.000000\n",
      "2019-05-15 07:00:00+00:00      0.000000\n",
      "2019-05-15 08:00:00+00:00      0.000000\n",
      "2019-05-15 09:00:00+00:00      0.000000\n",
      "2019-05-15 10:00:00+00:00      0.000000\n",
      "2019-05-15 11:00:00+00:00      0.000000\n",
      "2019-05-15 12:00:00+00:00      0.000000\n",
      "2019-05-15 13:00:00+00:00      9.228918\n",
      "2019-05-15 14:00:00+00:00    153.551454\n",
      "2019-05-15 15:00:00+00:00    382.053559\n",
      "2019-05-15 16:00:00+00:00    602.600405\n",
      "2019-05-15 17:00:00+00:00    790.599933\n",
      "2019-05-15 18:00:00+00:00    894.801787\n",
      "2019-05-15 19:00:00+00:00    868.868100\n",
      "Freq: H, dtype: float64\n",
      "18\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"7e810412-5d41-4354-9d73-a69003c21077\" data-root-id=\"6437\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
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   "source": [
    "# Complete GFS is available 5 hours after its initialization time\n",
    "run_time = pd.Timestamp('20190515T0500Z')\n",
    "issue_time = pd.Timestamp('20190515T0100Z')\n",
    "\n",
    "# define a Forecast object\n",
    "forecast = datamodel.Forecast(\n",
    "    name='OASIS DA GHI',\n",
    "    site=site,\n",
    "    variable='ghi',\n",
    "    interval_label='ending',\n",
    "    interval_value_type='mean',\n",
    "    interval_length='1h',\n",
    "    issue_time_of_day=datetime.time(hour=1),\n",
    "    run_length=pd.Timedelta('18h'),\n",
    "    lead_time_to_start=pd.Timedelta('0h')\n",
    ")\n",
    "\n",
    "# select the model\n",
    "model = models.gfs_quarter_deg_to_hourly_mean\n",
    "\n",
    "# tell the model where to find the NWP data\n",
    "model_wrapped = partial(model, load_forecast=load_forecast)\n",
    "\n",
    "# load and process the NWP data. ac_power is None because site is not a power plant.\n",
    "ghi, dni, dhi, air_temperature, wind_speed, ac_power = main.run_nwp(forecast, model_wrapped, run_time, issue_time)\n",
    "\n",
    "print(ghi), print(len(ghi))\n",
    "\n",
    "grid = plot_nwp(ghi, dni, dhi, air_temperature, wind_speed, ac_power)\n",
    "show(grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time\n",
      "2019-05-15 01:00:00+00:00     85.086198\n",
      "2019-05-15 02:00:00+00:00      0.488375\n",
      "2019-05-15 03:00:00+00:00      0.000000\n",
      "2019-05-15 04:00:00+00:00      0.000000\n",
      "2019-05-15 05:00:00+00:00      0.000000\n",
      "2019-05-15 06:00:00+00:00      0.000000\n",
      "2019-05-15 07:00:00+00:00      0.000000\n",
      "2019-05-15 08:00:00+00:00      0.000000\n",
      "2019-05-15 09:00:00+00:00      0.000000\n",
      "2019-05-15 10:00:00+00:00      0.000000\n",
      "2019-05-15 11:00:00+00:00      0.000000\n",
      "2019-05-15 12:00:00+00:00      5.616065\n",
      "2019-05-15 13:00:00+00:00    135.807230\n",
      "2019-05-15 14:00:00+00:00    362.832619\n",
      "2019-05-15 15:00:00+00:00    585.219414\n",
      "2019-05-15 16:00:00+00:00    776.573843\n",
      "2019-05-15 17:00:00+00:00    888.368133\n",
      "2019-05-15 18:00:00+00:00    873.486407\n",
      "Freq: H, dtype: float64\n",
      "18\n"
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temperature\"}},\"id\":\"7353\",\"type\":\"Line\"},{\"attributes\":{\"data_source\":{\"id\":\"7211\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"7352\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"7353\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"7355\",\"type\":\"CDSView\"}},\"id\":\"7354\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"ticker\":{\"id\":\"7222\",\"type\":\"DatetimeTicker\"}},\"id\":\"7225\",\"type\":\"Grid\"},{\"attributes\":{\"click_policy\":\"hide\",\"items\":[{\"id\":\"7267\",\"type\":\"LegendItem\"},{\"id\":\"7294\",\"type\":\"LegendItem\"},{\"id\":\"7321\",\"type\":\"LegendItem\"}],\"location\":\"top_left\"},\"id\":\"7266\",\"type\":\"Legend\"},{\"attributes\":{\"source\":{\"id\":\"7211\",\"type\":\"ColumnDataSource\"}},\"id\":\"7355\",\"type\":\"CDSView\"},{\"attributes\":{\"text\":\"\"},\"id\":\"7357\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"7359\",\"type\":\"DatetimeTickFormatter\"}],\"root_ids\":[\"7501\"]},\"title\":\"Bokeh Application\",\"version\":\"1.3.4\"}};\n",
       "  var render_items = [{\"docid\":\"c7e31dbb-28b8-444e-a151-a648f00d1955\",\"roots\":{\"7501\":\"d641df82-fbdd-4ebb-a1ee-acec40f6818f\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "7501"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Complete GFS is available 5 hours after its initialization time\n",
    "run_time = pd.Timestamp('20190515T0500Z')\n",
    "issue_time = pd.Timestamp('20190515T0100Z')\n",
    "\n",
    "# define a Forecast object\n",
    "forecast = datamodel.Forecast(\n",
    "    name='OASIS DA GHI',\n",
    "    site=site,\n",
    "    variable='ghi',\n",
    "    interval_label='beginning',\n",
    "    interval_value_type='mean',\n",
    "    interval_length='1h',\n",
    "    issue_time_of_day=datetime.time(hour=1),\n",
    "    run_length=pd.Timedelta('18h'),\n",
    "    lead_time_to_start=pd.Timedelta('0h')\n",
    ")\n",
    "\n",
    "# select the model\n",
    "model = models.gfs_quarter_deg_to_hourly_mean\n",
    "\n",
    "# tell the model where to find the NWP data\n",
    "model_wrapped = partial(model, load_forecast=load_forecast)\n",
    "\n",
    "# load and process the NWP data. ac_power is None because site is not a power plant.\n",
    "ghi, dni, dhi, air_temperature, wind_speed, ac_power = main.run_nwp(forecast, model_wrapped, run_time, issue_time)\n",
    "\n",
    "print(ghi), print(len(ghi))\n",
    "\n",
    "grid = plot_nwp(ghi, dni, dhi, air_temperature, wind_speed, ac_power)\n",
    "show(grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GFS interval labeling also works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The forecast function will produce an AC power forecast if supplied with the appropriate metadata. Here we define a single axis tracker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the modeling parameters\n",
    "modeling_parameters = datamodel.SingleAxisModelingParameters(\n",
    "    ac_capacity=10,  # always in MW\n",
    "    dc_capacity=13,\n",
    "    temperature_coefficient=-0.003,  # 1/C\n",
    "    dc_loss_factor=0,\n",
    "    ac_loss_factor=0,\n",
    "    axis_tilt=0,\n",
    "    axis_azimuth=0,\n",
    "    ground_coverage_ratio=0.4,\n",
    "    backtrack=True,\n",
    "    max_rotation_angle=50,\n",
    ")\n",
    "# define a SolarPowerPlant object\n",
    "plant = datamodel.SolarPowerPlant(\n",
    "    name='Tucson AZ Plant',\n",
    "    latitude=latitude,\n",
    "    longitude=longitude,\n",
    "    elevation=elevation,\n",
    "    timezone='America/Phoenix',\n",
    "    modeling_parameters=modeling_parameters\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we run the same code as before to create an AC power forecast from the GFS model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time\n",
      "2019-05-15 01:00:00+00:00     85.086198\n",
      "2019-05-15 02:00:00+00:00      0.488375\n",
      "2019-05-15 03:00:00+00:00      0.000000\n",
      "2019-05-15 04:00:00+00:00      0.000000\n",
      "2019-05-15 05:00:00+00:00      0.000000\n",
      "2019-05-15 06:00:00+00:00      0.000000\n",
      "2019-05-15 07:00:00+00:00      0.000000\n",
      "2019-05-15 08:00:00+00:00      0.000000\n",
      "2019-05-15 09:00:00+00:00      0.000000\n",
      "2019-05-15 10:00:00+00:00      0.000000\n",
      "2019-05-15 11:00:00+00:00      0.000000\n",
      "2019-05-15 12:00:00+00:00      5.616065\n",
      "2019-05-15 13:00:00+00:00    135.807230\n",
      "2019-05-15 14:00:00+00:00    362.832619\n",
      "2019-05-15 15:00:00+00:00    585.219414\n",
      "2019-05-15 16:00:00+00:00    776.573843\n",
      "2019-05-15 17:00:00+00:00    888.368133\n",
      "2019-05-15 18:00:00+00:00    873.486407\n",
      "Freq: H, dtype: float64\n",
      "18\n"
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speed\"},\"renderers\":[{\"id\":\"8532\",\"type\":\"GlyphRenderer\"}]},\"id\":\"8554\",\"type\":\"LegendItem\"}],\"root_ids\":[\"8701\"]},\"title\":\"Bokeh Application\",\"version\":\"1.3.4\"}};\n",
       "  var render_items = [{\"docid\":\"1f519ca8-392f-494f-b955-082fa2ddb2d3\",\"roots\":{\"8701\":\"779307de-4a2f-4f5d-8aae-9a9824f40de2\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "8701"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Complete GFS is available 5 hours after its initialization time\n",
    "run_time = pd.Timestamp('20190515T0500Z')\n",
    "issue_time = pd.Timestamp('20190515T0100Z')\n",
    "\n",
    "# define a Forecast object\n",
    "forecast = datamodel.Forecast(\n",
    "    name='OASIS DA GHI',\n",
    "    site=plant,\n",
    "    variable='ac_power',\n",
    "    interval_label='beginning',\n",
    "    interval_value_type='mean',\n",
    "    interval_length='1h',\n",
    "    issue_time_of_day=datetime.time(hour=1),\n",
    "    run_length=pd.Timedelta('18h'),\n",
    "    lead_time_to_start=pd.Timedelta('0h')\n",
    ")\n",
    "\n",
    "# select the model\n",
    "model = models.gfs_quarter_deg_to_hourly_mean\n",
    "\n",
    "# tell the model where to find the NWP data\n",
    "model_wrapped = partial(model, load_forecast=load_forecast)\n",
    "\n",
    "# load and process the NWP data. ac_power is None because site is not a power plant.\n",
    "ghi, dni, dhi, air_temperature, wind_speed, ac_power = main.run_nwp(forecast, model_wrapped, run_time, issue_time)\n",
    "\n",
    "print(ghi), print(len(ghi))\n",
    "\n",
    "grid = plot_nwp(ghi, dni, dhi, air_temperature, wind_speed, ac_power)\n",
    "show(grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Persistence forecasts\n",
    "\n",
    "The solarforecastarbiter supports several varieties of persistence forecasts:\n",
    "\n",
    "1. Persistence of observed values\n",
    "2. Persistence of irradiance or power accounting for solar position. (This is sometimes referred to as smart persistence, but we discourage that term because it's ambiguous.)\n",
    "\n",
    "The [`persistence`](https://solarforecastarbiter-core.readthedocs.io/en/latest/generated/solarforecastarbiter.reference_forecasts.persistence.html#module-solarforecastarbiter.reference_forecasts.persistence) module contains functions that implement these methods and are easily adapted to a user's own data and workflow. The [`reference_forecasts.main.run_persistence`](https://solarforecastarbiter-core.readthedocs.io/en/latest/generated/solarforecastarbiter.reference_forecasts.main.run_persistence.html#solarforecastarbiter.reference_forecasts.main.run_persistence) function is a higher-level interface that integrate more tightly with the Solar Forecast Arbiter data model and API."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### run_persistence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below queries the API for observation metadata and data. We'll use the NREL MIDC OASIS station located on the University of Arizona campus. See the [Data Upload and Download]() exercises for more information on the data transfer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Observation(name='University of Arizona OASIS ghi', variable='ghi', interval_value_type='interval_mean', interval_length=Timedelta('0 days 00:01:00'), interval_label='ending', site=Site(name='NREL MIDC University of Arizona OASIS', latitude=32.22969, longitude=-110.95534, elevation=786.0, timezone='Etc/GMT+7', site_id='9f61b880-7e49-11e9-9624-0a580a8003e9', provider='Reference', extra_parameters='{\"network\": \"NREL MIDC\", \"network_api_id\": \"UAT\", \"network_api_abbreviation\": \"UA OASIS\", \"observation_interval_length\": 1}'), uncertainty=0.0, observation_id='9f657636-7e49-11e9-b77f-0a580a8003e9', extra_parameters='{\"network\": \"NREL MIDC\", \"network_api_id\": \"UAT\", \"network_api_abbreviation\": \"UA OASIS\", \"observation_interval_length\": 1, \"network_data_label\": \"Global Horiz (platform) [W/m^2]\"}', units='W/m^2')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from solarforecastarbiter.io.api import APISession, request_cli_access_token\n",
    "# don't store your real passwords or tokens in plain text like this! only for demonstration purposes!\n",
    "token = request_cli_access_token('testing@solarforecastarbiter.org', 'Thepassword123!')\n",
    "session = APISession(token)\n",
    "observations = session.list_observations()\n",
    "observations_oasis = list(filter(lambda x: 'NREL MIDC' in x.site.name and 'Arizona' in x.site.name, observations))\n",
    "oasis_ghi = observations_oasis[0]\n",
    "oasis_ghi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>value</th>\n",
       "      <th>quality_flag</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-15 00:00:00+00:00</th>\n",
       "      <td>428.807</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-15 00:01:00+00:00</th>\n",
       "      <td>425.372</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-15 00:02:00+00:00</th>\n",
       "      <td>422.223</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-15 00:03:00+00:00</th>\n",
       "      <td>418.949</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-15 00:04:00+00:00</th>\n",
       "      <td>414.445</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             value  quality_flag\n",
       "timestamp                                       \n",
       "2019-05-15 00:00:00+00:00  428.807             2\n",
       "2019-05-15 00:01:00+00:00  425.372             2\n",
       "2019-05-15 00:02:00+00:00  422.223             2\n",
       "2019-05-15 00:03:00+00:00  418.949             2\n",
       "2019-05-15 00:04:00+00:00  414.445             2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = pd.Timestamp('20190515 0000Z')\n",
    "end = pd.Timestamp('20190518 0000Z')\n",
    "oasis_ghi_values = session.get_observation_values(oasis_ghi.observation_id, start, end)\n",
    "oasis_ghi_values.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our example persistence forecast will be a 15 minute ahead, 15 minute interval average forecast for the NREL MIDC OASIS GHI observation. Both the observation's and forecast's intervals are labeled by their ending time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast = datamodel.Forecast(\n",
    "    name='15 min ahead, 15 min avg', \n",
    "    issue_time_of_day=datetime.time(0), \n",
    "    lead_time_to_start=pd.Timedelta('15min'),\n",
    "    interval_length=pd.Timedelta('15min'),\n",
    "    run_length=pd.Timedelta('15min'),\n",
    "    interval_label='ending',\n",
    "    interval_value_type='interval_mean',\n",
    "    variable='ghi',\n",
    "    site=oasis_ghi\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resample the observed 1 minute interval data to have the same interval length as the forecasts for a fair comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "resampled_data = oasis_ghi_values.resample(forecast.interval_length, label='right').mean()\n",
    "resampled_data['quality_flag'] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a forecast `run_time` and `issue_time`. For operational forecasts, `run_time` is typically set to now. For retrospective forecasts, `run_time` is the time by which the forecast should be run so that it could have been be delivered for the `issue_time`. Forecasts will only use data with timestamps before `run_time`. Let's assume they are the same for this exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_time = pd.Timestamp('20190515 1900Z')\n",
    "issue_time = pd.Timestamp('20190515 1900Z')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the observation values leading up to the forecast run time. We expect the persistence forecast to be equal to an average of these values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              value  quality_flag\n",
      "timestamp                                        \n",
      "2019-05-15 18:45:00+00:00  1059.300             2\n",
      "2019-05-15 18:46:00+00:00  1059.250             2\n",
      "2019-05-15 18:47:00+00:00  1058.190             2\n",
      "2019-05-15 18:48:00+00:00  1059.600             2\n",
      "2019-05-15 18:49:00+00:00  1059.650             2\n",
      "2019-05-15 18:50:00+00:00  1062.410             2\n",
      "2019-05-15 18:51:00+00:00  1043.830             2\n",
      "2019-05-15 18:52:00+00:00  1053.610             2\n",
      "2019-05-15 18:53:00+00:00  1082.200             2\n",
      "2019-05-15 18:54:00+00:00  1087.860             2\n",
      "2019-05-15 18:55:00+00:00  1097.610             2\n",
      "2019-05-15 18:56:00+00:00  1104.210             2\n",
      "2019-05-15 18:57:00+00:00  1069.280             2\n",
      "2019-05-15 18:58:00+00:00   897.564             2\n",
      "2019-05-15 18:59:00+00:00   832.101             2\n",
      "2019-05-15 19:00:00+00:00   916.081             2\n",
      "\n",
      " mean:\n",
      "value           1033.921625\n",
      "quality_flag       2.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(oasis_ghi_values.loc[run_time - forecast.interval_length:run_time])\n",
    "print('\\n mean:')\n",
    "print(oasis_ghi_values.loc[run_time - forecast.interval_length:run_time].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-05-15 19:30:00+00:00    1033.921625\n",
       "Freq: 15T, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oasis_ghi_fx = main.run_persistence(session, oasis_ghi, forecast, run_time, issue_time, index=False)\n",
    "oasis_ghi_fx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It works! \n",
    "\n",
    "There is a problem, though. A 15 minute interval should include *either* the start time, value pair *or* the end time, value pair, but *not* both. This bug is tracked in [this GitHub issue](https://github.com/SolarArbiter/solarforecastarbiter-core/issues/80). The resampled data that we'll use for plotting actually shows the correct result. The correct result is slightly below than the incorrect result because it discards the above average value that marks the end of the last interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "value           1032.537933\n",
       "quality_flag       0.000000\n",
       "Name: 2019-05-15 18:45:00+00:00, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled_data.loc[run_time - forecast.interval_length]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make a [forecast evaluation time series](https://solarforecastarbiter.org/usecases/#forecastevalts), we need to loop over many run times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_times = pd.date_range(start=start, end=end, freq=forecast.run_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "oasis_ghi_fx_runs = []\n",
    "for run_time in run_times:\n",
    "    issue_time = run_time  # an offset may be needed in an operational environment\n",
    "    fx_run = main.run_persistence(session, oasis_ghi, forecast, run_time, issue_time, index=False)\n",
    "    oasis_ghi_fx_runs.append(fx_run)\n",
    "oasis_ghi_fx = pd.concat(oasis_ghi_fx_runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_persistence(observation_values, forecast_values=None, forecast_values_index=None):\n",
    "    fig_kwargs = dict(tools=TOOLS, x_axis_type=\"datetime\", plot_height=250, plot_width=800)\n",
    "    palette = iter(PALETTE)\n",
    "    fig1 = figure(**fig_kwargs)\n",
    "    fig1.line(observation_values.index, observation_values['value'], legend='observed', color='black', line_width=2)\n",
    "    color = next(palette)\n",
    "    if forecast_values is not None:\n",
    "        fig1.line(forecast_values.index, forecast_values, legend='persistence', color=color, line_width=2)\n",
    "    color = next(palette)\n",
    "    if forecast_values_index is not None:\n",
    "        fig1.line(forecast_values_index.index, forecast_values_index, legend='persistence index', color=color, line_width=2)\n",
    "    fig1.yaxis.axis_label = \"Irradiance (W/m^2)\"\n",
    "    fig1.legend.location = \"top_left\"\n",
    "    fig1.xaxis.axis_label = 'Time (UTC)'\n",
    "    return fig1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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TaEWFWCMC9UMB2MOIHwIdu4zyJdQjA9+s7rF2ACMAd6KG2DSMGwG20CR1h/gXApU3VPbL5BcBHVKhuLr4GwAtgysABLQbAjbrDE2fyBcAexM4UOu8FwD20ouA87wXAZiPXTSnvBcB2G8GzKu8FwNvsIl8c7wXAI0c6AyPvBcDx2xDjNe8FwH/CxuIkOgbA4ka3SwgDBMBL7i862JrkP5bjKFPffCpA7VcUiRyFPEDglMzEh6xUQDpFR3L5M2FArNIDDLgQaEDb/KwfiGVvQEIWj3ifRHNA8x/Sb1+rdkA830+Nly56QAR13W5c031AK6xBhRC7gEDRkVz+Q/KEQI5NGLr/8IRAcfbhM+zsd0BsCfmgZ+WDQKO6k2Jain5AZBa0o0sUdkC6kDSl4nWFQNxoAG+BloVAPeJ9QmPhi0Cn63bjypKPQDpqch47RolAgQfzRP2djEC+MJkqGF2MQLWjSxJed4lAzYJ2dMmckEDVvxHK6JaQQFxImlJxdZBAH4XrUbg8kEBy9uEz7ByQQKEaL90k/o9AxwQP5okIkEAis6AdXRyOQJoF7eiSVY1ABH5njr+UjEBF2PD0SsaMQETVwj9/QYtAAp020GmtikA9VC38812JQFG4HoXr64dAjUrqBDRshkAa4lgXtweFQA3//NFKgYNAOdbFbTTfgUBsv6JI5D6AQGWIY13cEn1AHVpkO98ve0A=\",\"dtype\":\"float64\",\"shape\":[289]}},\"selected\":{\"id\":\"3595\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"3596\",\"type\":\"UnionRenderers\"}},\"id\":\"3545\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"3816\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"3595\",\"type\":\"Selection\"},{\"attributes\":{\"source\":{\"id\":\"3545\",\"type\":\"ColumnDataSource\"}},\"id\":\"3549\",\"type\":\"CDSView\"},{\"attributes\":{\"items\":[{\"id\":\"3570\",\"type\":\"LegendItem\"},{\"id\":\"3597\",\"type\":\"LegendItem\"}],\"location\":\"top_left\"},\"id\":\"3569\",\"type\":\"Legend\"},{\"attributes\":{},\"id\":\"3815\",\"type\":\"Selection\"},{\"attributes\":{\"source\":{\"id\":\"3571\",\"type\":\"ColumnDataSource\"}},\"id\":\"3575\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"3537\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"3596\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"3562\",\"type\":\"DaysTicker\"},{\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]},\"id\":\"3564\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"data_source\":{\"id\":\"3571\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"3572\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"3573\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"3575\",\"type\":\"CDSView\"}},\"id\":\"3574\",\"type\":\"GlyphRenderer\"}],\"root_ids\":[\"3515\"]},\"title\":\"Bokeh Application\",\"version\":\"1.2.0\"}};\n",
       "  var render_items = [{\"docid\":\"94c59de6-9b1a-47fb-a2e3-a9ceae6fc90a\",\"roots\":{\"3515\":\"45fbc16e-dec1-4772-a4a2-2b2bbec8e59d\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "3515"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plot_persistence(resampled_data, oasis_ghi_fx)\n",
    "show(fig1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the persistence forecast appears to be a simple translation in time of the input data.\n",
    "\n",
    "Next we will examine forecasts using persistence of clear sky index to account for changing solar position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "oasis_ghi_fx_runs = []\n",
    "for run_time in run_times:\n",
    "    issue_time = run_time  # an offset may be needed in an operational environment\n",
    "    fx_run = main.run_persistence(session, oasis_ghi, forecast, run_time, issue_time, index=True)\n",
    "    oasis_ghi_fx_runs.append(fx_run)\n",
    "oasis_ghi_index_fx = pd.concat(oasis_ghi_fx_runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"010297a8-3926-4a01-bf31-89dbdc0c323c\" data-root-id=\"3955\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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     "output_type": "display_data"
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   ],
   "source": [
    "fig1 = plot_persistence(resampled_data, forecast_values_index=oasis_ghi_index_fx)\n",
    "show(fig1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The persistence of clear sky index accounts for the change in solar position. It also introduces errors near sunrise when the ratio of actual to expected clear sky depends sensitively on the instrumentation and the clear sky model.\n",
    "\n",
    "\"Day ahead persistence\" is another flavor of persistence in which the observed values for a 24 hour period are used as the forecast for another 24 hour period. The Solar Forecast Arbiter's [`persistence_interval`](https://solarforecastarbiter-core.readthedocs.io/en/latest/generated/solarforecastarbiter.reference_forecasts.persistence.persistence_interval.html#solarforecastarbiter.reference_forecasts.persistence.persistence_interval) function implements this feature. The high-level interface currently does not work with the function due to the same [bug](https://github.com/SolarArbiter/solarforecastarbiter-core/issues/80) discussed above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "da_forecast = datamodel.Forecast(\n",
    "    name='day ahead persistence', \n",
    "    issue_time_of_day=datetime.time(7),  # local midnight\n",
    "    lead_time_to_start=pd.Timedelta('0min'),\n",
    "    interval_length=pd.Timedelta('1h'),\n",
    "    run_length=pd.Timedelta('24hr'),\n",
    "    interval_label='beginning',\n",
    "    interval_value_type='interval_mean',\n",
    "    variable='ghi',\n",
    "    site=oasis_ghi\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of passed values is 25, index implies 24\n"
     ]
    }
   ],
   "source": [
    "run_time = pd.Timestamp('20190516 0000-0700')\n",
    "issue_time = run_time\n",
    "try:\n",
    "    fx_run = main.run_persistence(session, oasis_ghi, da_forecast, run_time, issue_time, index=False)\n",
    "except ValueError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### persistence module\n",
    "\n",
    "This section demonstrates the lower level persistence functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from solarforecastarbiter.reference_forecasts import persistence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The functions in the persistence module are meant to be easily useable by the community. To facilitate this, the persistence functions allow a user to provide a function that will load their data rather than loading data from the Solar Forecast Arbiter API. Below, we define a function that will load the appropriate data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change default load_data function so we can use without API access\n",
    "def load_data_base(data, observation, data_start, data_end):\n",
    "    # slice doesn't care about closed or interval label \n",
    "    # so here we manually adjust start and end times\n",
    "    if 'instant' in observation.interval_label:\n",
    "        pass\n",
    "    elif observation.interval_label == 'ending':\n",
    "        data_start += pd.Timedelta('1s')\n",
    "    elif observation.interval_label == 'beginning':\n",
    "        data_end -= pd.Timedelta('1s')\n",
    "    return data[data_start:data_end]\n",
    "\n",
    "load_data = partial(load_data_base, oasis_ghi_values['value'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below makes two forecasts, one for persistence of the observed GHI (`persistence_scalar`) and one using persistence of the clear sky index (`persistence_scalar_index`). Notice how the persistence index forecast accounts for the changing solar position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation = oasis_ghi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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Application\",\"version\":\"1.2.0\"}};\n",
       "  var render_items = [{\"docid\":\"dd64434e-4851-4d63-9c8b-17ac0bbad1c6\",\"roots\":{\"4418\":\"41d08d8d-c203-48a6-9584-a4a092d85052\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "4418"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# persistence forecasts with scalar index\n",
    "# observation.interval_label = 'ending', \n",
    "# so data ranges are exclusive of start and inclusive of end\n",
    "data_start = pd.Timestamp('20190515 1500Z')\n",
    "data_end = pd.Timestamp('20190515 1600Z')\n",
    "forecast_start = pd.Timestamp('20190515 1600Z')\n",
    "forecast_end = pd.Timestamp('20190515 1700Z')\n",
    "interval_length = pd.Timedelta('5min')\n",
    "interval_label = 'ending'\n",
    "\n",
    "fx_per = persistence.persistence_scalar(\n",
    "    observation, data_start, data_end, forecast_start,\n",
    "    forecast_end, interval_length, interval_label, load_data=load_data)\n",
    "fx_per_idx = persistence.persistence_scalar_index(\n",
    "    observation, data_start, data_end, forecast_start,\n",
    "    forecast_end, interval_length, interval_label, load_data=load_data)\n",
    "\n",
    "resampled_data = oasis_ghi_values.resample(interval_length, label='right').mean()\n",
    "fig = plot_persistence(resampled_data, fx_per, fx_per_idx)\n",
    "show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same data and forecast methods but for a cloudy period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"c4290af8-c330-4bc9-b438-43b44492f34b\" data-root-id=\"4947\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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(W/m^2)\",\"formatter\":{\"id\":\"4984\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"4962\",\"type\":\"BasicTicker\"}},\"id\":\"4961\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"4952\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"5056\",\"type\":\"Selection\"},{\"attributes\":{\"source\":{\"id\":\"4977\",\"type\":\"ColumnDataSource\"}},\"id\":\"4981\",\"type\":\"CDSView\"},{\"attributes\":{\"text\":\"\"},\"id\":\"4982\",\"type\":\"Title\"},{\"attributes\":{\"dimensions\":\"width\"},\"id\":\"4968\",\"type\":\"WheelZoomTool\"}],\"root_ids\":[\"4947\"]},\"title\":\"Bokeh Application\",\"version\":\"1.2.0\"}};\n",
       "  var render_items = [{\"docid\":\"9dbf5d65-6047-4e36-a581-177171074273\",\"roots\":{\"4947\":\"c4290af8-c330-4bc9-b438-43b44492f34b\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "4947"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# persistence forecasts with scalar index\n",
    "# observation.interval_label = 'ending', \n",
    "# so data ranges are exclusive of start and inclusive of end\n",
    "data_start = pd.Timestamp('20190515 1700Z')\n",
    "data_end = pd.Timestamp('20190515 1800Z')\n",
    "forecast_start = pd.Timestamp('20190515 1800Z')\n",
    "forecast_end = pd.Timestamp('20190515 1900Z')\n",
    "interval_length = pd.Timedelta('5min')\n",
    "interval_label = 'ending'\n",
    "\n",
    "fx_per = persistence.persistence_scalar(\n",
    "    observation, data_start, data_end, forecast_start,\n",
    "    forecast_end, interval_length, interval_label, load_data=load_data)\n",
    "fx_per_idx = persistence.persistence_scalar_index(\n",
    "    observation, data_start, data_end, forecast_start,\n",
    "    forecast_end, interval_length, interval_label, load_data=load_data)\n",
    "\n",
    "resampled_data = oasis_ghi_values.resample(interval_length, label='right').mean()\n",
    "fig = plot_persistence(resampled_data, fx_per, fx_per_idx)\n",
    "show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The \"day ahead persistence\" function [`persistence_interval`](https://solarforecastarbiter-core.readthedocs.io/en/latest/generated/solarforecastarbiter.reference_forecasts.persistence.persistence_interval.html#solarforecastarbiter.reference_forecasts.persistence.persistence_interval) works here because our example `load_data` function accounts for interval labeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "(function(root) {\n",
       "  function embed_document(root) {\n",
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       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
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       "  if (root.Bokeh !== undefined) {\n",
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       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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       "  }\n",
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      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
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       "id": "5501"
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     "output_type": "display_data"
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   "source": [
    "# persistence forecasts with scalar index\n",
    "# observation.interval_label = 'ending', \n",
    "# so data ranges are exclusive of start and inclusive of end\n",
    "data_start = pd.Timestamp('20190515 0600Z')\n",
    "data_end = pd.Timestamp('20190516 0600Z')\n",
    "forecast_start = pd.Timestamp('20190516 0600Z')\n",
    "interval_length = pd.Timedelta('1hr')\n",
    "interval_label = 'ending'\n",
    "\n",
    "fx_per_interval = persistence.persistence_interval(\n",
    "    observation, data_start, data_end, forecast_start,\n",
    "    interval_length, interval_label, load_data=load_data)\n",
    "\n",
    "resampled_data = oasis_ghi_values.resample(interval_length, label='right').mean()\n",
    "fig = plot_persistence(resampled_data, fx_per_interval)\n",
    "show(fig)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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