reference_forecasts.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\n",
    "from bokeh.plotting import figure, show\n",
    "from bokeh import palettes\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": "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": 5,
   "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='interval_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": 6,
   "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)\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
       "Freq: 15T, Name: ghi, dtype: float32"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ghi.head()"
   ]
  },
  {
   "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": 8,
   "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(np.nan, 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_label=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_label=\"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_label=\"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_label=\"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",
    "    return grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function to plot the HRRR data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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      "application/vnd.bokehjs_exec.v0+json": ""
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       "id": "1292"
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     "output_type": "display_data"
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   ],
   "source": [
    "grid = plot_nwp(ghi, dni, dhi, air_temperature, wind_speed, ac_power)\n",
    "show(grid)"
   ]
  },
  {
   "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. Backfill cloud cover to 5 minute resolution and interpolate air temperature, wind speed to 5 minute resolution. This is needed to account for changing solar position over 1 hour intervals.\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": 10,
   "metadata": {},
   "outputs": [
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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 3 days GHI',\n",
    "    site=site,\n",
    "    variable='ghi',\n",
    "    interval_label='instant',\n",
    "    interval_value_type='interval_mean',\n",
    "    interval_length='1h',\n",
    "    issue_time_of_day=datetime.time(hour=1),\n",
    "    run_length=pd.Timedelta('24h')*3,\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",
    "grid = plot_nwp(ghi, dni, dhi, air_temperature, wind_speed, ac_power)\n",
    "show(grid)"
   ]
  },
  {
   "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": 11,
   "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": 12,
   "metadata": {},
   "outputs": [
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       "  var render_items = [{\"docid\":\"eee2815e-a9e1-45bb-ad21-85ae667e6045\",\"roots\":{\"2844\":\"4a773a66-560d-4249-864f-2fc36550bd11\"}}];\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",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "2844"
      }
     },
     "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 3 days GHI',\n",
    "    site=plant,\n",
    "    variable='ghi',\n",
    "    interval_label='instant',\n",
    "    interval_value_type='interval_mean',\n",
    "    interval_length='1h',\n",
    "    issue_time_of_day=datetime.time(hour=1),\n",
    "    run_length=pd.Timedelta('24h')*3,\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",
    "grid = plot_nwp(ghi, dni, dhi, air_temperature, wind_speed, ac_power)\n",
    "show(grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Probabilistic forecasts derived from the GEFS model can be obtained from the `gefs_half_deg_to_hourly_mean` function. The returned values are hourly averages sorted from smallest to largest at each time stamp. Each variable is sorted independently. This describes a `ProbabilisticForecast` with `axis='x'` and `constant_values=[0, 5, ...95, 100]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Complete GEFS is available 6 hours after its initialization time\n",
    "run_time = pd.Timestamp('20190515T0600Z')\n",
    "issue_time = pd.Timestamp('20190515T0100Z')\n",
    "\n",
    "# define a ProbablisticForecast object in 3 steps.\n",
    "# 1. define common metadata\n",
    "forecast_metadata = dict(\n",
    "    name='OASIS 3 days GHI GEFS',\n",
    "    site=plant,\n",
    "    variable='ghi',\n",
    "    interval_label='instant',\n",
    "    interval_value_type='interval_mean',\n",
    "    interval_length='60',\n",
    "    issue_time_of_day='01:00',\n",
    "    run_length='4320',\n",
    "    lead_time_to_start='0'\n",
    ")\n",
    "\n",
    "# 2. define metadata for the forecast for each percentile\n",
    "#    these are known as ProbabilisticForecastConstantValue objects\n",
    "probfx_constantvalues = []\n",
    "for cv in range(0, 101, 5):\n",
    "    d = forecast_metadata.copy()\n",
    "    d['axis'] = 'x'\n",
    "    d['constant_value'] = cv\n",
    "    probfx_constantvalues.append(d)\n",
    "    \n",
    "# 3. define metadata for the collection of percentile forecasts\n",
    "#    this is the ProbabilisticForecast\n",
    "d = forecast_metadata.copy()\n",
    "d['axis'] = 'x'\n",
    "d['constant_values'] = probfx_constantvalues\n",
    "forecast = datamodel.ProbabilisticForecast.from_dict(d)\n",
    "\n",
    "# select the model\n",
    "model = models.gefs_half_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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the AC power dataframe and then plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "                                 0         1         2          3          4   \\\n",
       "2019-05-15 01:00:00+00:00  0.652374  1.033796  1.169189   1.218102   1.616828   \n",
       "2019-05-15 02:00:00+00:00  0.000000  0.000000  0.000000   0.000000   0.000000   \n",
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       "...                             ...       ...       ...        ...        ...   \n",
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       "\n",
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       "2019-05-18 00:00:00+00:00   8.908472  ...   8.908539   8.908602   8.908609   \n",
       "2019-05-18 01:00:00+00:00   5.978787  ...   5.978848   5.978925   5.978926   \n",
       "\n",
       "                                  14         15         16         17  \\\n",
       "2019-05-15 01:00:00+00:00   2.156442   2.156476   2.196468   2.237010   \n",
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       "...                              ...        ...        ...        ...   \n",
       "2019-05-17 21:00:00+00:00  10.000000  10.000000  10.000000  10.000000   \n",
       "2019-05-17 22:00:00+00:00  10.000000  10.000000  10.000000  10.000000   \n",
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       "2019-05-18 00:00:00+00:00   8.908623   8.908626   8.908655   8.908662   \n",
       "2019-05-18 01:00:00+00:00   5.978938   5.978944   5.978973   5.978973   \n",
       "\n",
       "                                  18         19         20  \n",
       "2019-05-15 01:00:00+00:00   2.237052   2.237058   2.278044  \n",
       "2019-05-15 02:00:00+00:00   0.000000   0.000000   0.000000  \n",
       "2019-05-15 03:00:00+00:00   0.000000   0.000000   0.000000  \n",
       "2019-05-15 04:00:00+00:00   0.000000   0.000000   0.000000  \n",
       "2019-05-15 05:00:00+00:00   0.000000   0.000000   0.000000  \n",
       "...                              ...        ...        ...  \n",
       "2019-05-17 21:00:00+00:00  10.000000  10.000000  10.000000  \n",
       "2019-05-17 22:00:00+00:00  10.000000  10.000000  10.000000  \n",
       "2019-05-17 23:00:00+00:00  10.000000  10.000000  10.000000  \n",
       "2019-05-18 00:00:00+00:00   8.908763   8.908839   8.908846  \n",
       "2019-05-18 01:00:00+00:00   5.979082   5.979160   5.979160  \n",
       "\n",
       "[73 rows x 21 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ac_power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_percentiles(data):\n",
    "    data = data.rename(columns=lambda x: str(x))\n",
    "    source = ColumnDataSource(data)\n",
    "\n",
    "    figs = []\n",
    "    fig_kwargs = dict(tools=TOOLS, x_axis_type=\"datetime\", plot_height=200)\n",
    "    palette = iter(palettes.viridis(len(data.columns)))\n",
    "    \n",
    "    # irradiance figure\n",
    "    fig1 = figure(**fig_kwargs)\n",
    "    for p in data.keys():\n",
    "        fig1.line(x='index', y=p, source=source, legend_label=p, color=next(palette), line_width=2)\n",
    "    fig1.yaxis.axis_label = \"AC power (MW)\"\n",
    "    fig1.legend.location = \"top_left\"\n",
    "    figs.append(fig1)\n",
    "    \n",
    "    grid = gridplot(figs, ncols=1, plot_width=800)\n",
    "    return grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"cf79ee5e-8c40-4a85-bb0e-116b6056d804\" data-root-id=\"4137\"></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\":\"924c09fa-7d89-46bb-9032-eeba8b607450\",\"roots\":{\"4137\":\"cf79ee5e-8c40-4a85-bb0e-116b6056d804\"}}];\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",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "4137"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = plot_percentiles(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": 17,
   "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}', climate_zones=('Reference Region 3',)), uncertainty=0.0, observation_id='9f657636-7e49-11e9-b77f-0a580a8003e9', provider='Reference', 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": 17,
     "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": 18,
   "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>2019-05-14 00:00:00+00:00</th>\n",
       "      <td>415.039</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-14 00:01:00+00:00</th>\n",
       "      <td>411.473</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-14 00:02:00+00:00</th>\n",
       "      <td>407.793</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-14 00:03:00+00:00</th>\n",
       "      <td>404.119</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-14 00:04:00+00:00</th>\n",
       "      <td>400.527</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             value  quality_flag\n",
       "timestamp                                       \n",
       "2019-05-14 00:00:00+00:00  415.039             2\n",
       "2019-05-14 00:01:00+00:00  411.473             2\n",
       "2019-05-14 00:02:00+00:00  407.793             2\n",
       "2019-05-14 00:03:00+00:00  404.119             2\n",
       "2019-05-14 00:04:00+00:00  400.527             2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = pd.Timestamp('20190514 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": 19,
   "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": 20,
   "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": 21,
   "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. Remember, though, that a 15 minute interval should include *either* the start time *or* the end time, but *not* both. The forecast we're considering has *interval_label='ending'*, so we exclude the start and include the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              value  quality_flag\n",
      "timestamp                                        \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           1032.229733\n",
      "quality_flag       2.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(oasis_ghi_values.loc[run_time + pd.Timedelta('1s') - forecast.interval_length:run_time])\n",
    "print('\\n mean:')\n",
    "print(oasis_ghi_values.loc[run_time + pd.Timedelta('1s') - forecast.interval_length:run_time].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now use the `run_persistence` function to automatically compute this forecast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-05-15 19:30:00+00:00    1032.229733\n",
       "Freq: 15T, dtype: float64"
      ]
     },
     "execution_count": 23,
     "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": [
    "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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_times = pd.date_range(start=start, end=end, freq=forecast.run_length)"
   ]
  },
  {
   "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=False)\n",
    "    oasis_ghi_fx_runs.append(fx_run)\n",
    "oasis_ghi_fx = pd.concat(oasis_ghi_fx_runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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_label='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_label='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_label='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": 27,
   "metadata": {},
   "outputs": [
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(UTC)\",\"formatter\":{\"id\":\"4521\",\"type\":\"DatetimeTickFormatter\"},\"ticker\":{\"id\":\"4491\",\"type\":\"DatetimeTicker\"}},\"id\":\"4490\",\"type\":\"DatetimeAxis\"},{\"attributes\":{\"items\":[{\"id\":\"4536\",\"type\":\"LegendItem\"},{\"id\":\"4563\",\"type\":\"LegendItem\"}],\"location\":\"top_left\"},\"id\":\"4535\",\"type\":\"Legend\"},{\"attributes\":{},\"id\":\"4504\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"4503\",\"type\":\"ResetTool\"},{\"attributes\":{\"num_minor_ticks\":5,\"tickers\":[{\"id\":\"4522\",\"type\":\"AdaptiveTicker\"},{\"id\":\"4523\",\"type\":\"AdaptiveTicker\"},{\"id\":\"4524\",\"type\":\"AdaptiveTicker\"},{\"id\":\"4525\",\"type\":\"DaysTicker\"},{\"id\":\"4526\",\"type\":\"DaysTicker\"},{\"id\":\"4527\",\"type\":\"DaysTicker\"},{\"id\":\"4528\",\"type\":\"DaysTicker\"},{\"id\":\"4529\",\"type\":\"MonthsTicker\"},{\"id\":\"4530\",\"type\":\"MonthsTicker\"},{\"id\":\"4531\",\"type\":\"MonthsTicker\"},{\"id\":\"4532\",\"type\":\"MonthsTicker\"},{\"id\":\"4533\",\"type\":\"YearsTicker\"}]},\"id\":\"4491\",\"type\":\"DatetimeTicker\"},{\"attributes\":{\"overlay\":{\"id\":\"4534\",\"type\":\"BoxAnnotation\"}},\"id\":\"4501\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"4496\",\"type\":\"BasicTicker\"}},\"id\":\"4499\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"4500\",\"type\":\"PanTool\"}],\"root_ids\":[\"4481\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"d1ed7752-bdc2-4c59-b4ac-55161837d5de\",\"roots\":{\"4481\":\"530c889e-60c2-43a6-a131-cffded0755a3\"}}];\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",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "4481"
      }
     },
     "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": 28,
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"e06e6a97-c761-4830-b1cf-5e102f3f2115\" data-root-id=\"4942\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
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\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"5287\",\"type\":\"UnionRenderers\"},{\"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\":\"4995\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"label\":{\"value\":\"persistence 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       "  var render_items = [{\"docid\":\"c841860b-db69-4458-b64a-003570916340\",\"roots\":{\"4942\":\"e06e6a97-c761-4830-b1cf-5e102f3f2115\"}}];\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",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "4942"
      }
     },
     "output_type": "display_data"
    }
   ],
   "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. For a 0 lead time forecast, data will be pulled from 48 hours up to 24 hours ago. 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "da_forecast = datamodel.Forecast(\n",
    "    name='day ahead persistence', \n",
    "    issue_time_of_day=datetime.time(7),  # must be in UTC\n",
    "    lead_time_to_start=pd.Timedelta('0min'),\n",
    "    interval_length=pd.Timedelta('15min'),\n",
    "    run_length=pd.Timedelta('24hr'),\n",
    "    interval_label='beginning',\n",
    "    interval_value_type='interval_mean',\n",
    "    variable='ghi',\n",
    "    site=oasis_ghi\n",
    ")\n",
    "\n",
    "run_time = pd.Timestamp('20190516 0000-0700')\n",
    "issue_time = run_time\n",
    "fx_run = main.run_persistence(session, oasis_ghi, da_forecast, run_time, issue_time, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "  <div class=\"bk-root\" id=\"ac8d0998-7dc3-45ba-9146-02e1b9f54931\" data-root-id=\"5426\"></div>\n"
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     "metadata": {},
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    {
     "data": {
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       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
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       "  var render_items = [{\"docid\":\"352e0d78-76c5-4fb9-9dfc-f5879488bd55\",\"roots\":{\"5426\":\"ac8d0998-7dc3-45ba-9146-02e1b9f54931\"}}];\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",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "5426"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plot_persistence(resampled_data, fx_run)\n",
    "show(fig1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### persistence module\n",
    "\n",
    "This section demonstrates the lower level persistence functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 33,
   "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": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation = oasis_ghi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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     },
     "metadata": {},
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       "  function embed_document(root) {\n",
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\",\"dtype\":\"float64\",\"shape\":[1153]}},\"selected\":{\"id\":\"6013\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"6014\",\"type\":\"UnionRenderers\"}},\"id\":\"5963\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"months\":[0,6]},\"id\":\"5984\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"source\":{\"id\":\"6016\",\"type\":\"ColumnDataSource\"}},\"id\":\"6020\",\"type\":\"CDSView\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"5979\",\"type\":\"DaysTicker\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"6018\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"5985\",\"type\":\"YearsTicker\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"5952\",\"type\":\"PanTool\"},{\"id\":\"5953\",\"type\":\"BoxZoomTool\"},{\"id\":\"5954\",\"type\":\"WheelZoomTool\"},{\"id\":\"5955\",\"type\":\"ResetTool\"},{\"id\":\"5956\",\"type\":\"SaveTool\"}]},\"id\":\"5957\",\"type\":\"Toolbar\"},{\"attributes\":{\"label\":{\"value\":\"persistence index\"},\"renderers\":[{\"id\":\"6019\",\"type\":\"GlyphRenderer\"}]},\"id\":\"6044\",\"type\":\"LegendItem\"},{\"attributes\":{\"months\":[0,4,8]},\"id\":\"5983\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"source\":{\"id\":\"5989\",\"type\":\"ColumnDataSource\"}},\"id\":\"5993\",\"type\":\"CDSView\"},{\"attributes\":{\"dimensions\":\"width\"},\"id\":\"5954\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"5956\",\"type\":\"SaveTool\"},{\"attributes\":{\"axis_label\":\"Irradiance (W/m^2)\",\"formatter\":{\"id\":\"5971\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"5948\",\"type\":\"BasicTicker\"}},\"id\":\"5947\",\"type\":\"LinearAxis\"},{\"attributes\":{\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"5964\",\"type\":\"Line\"},{\"attributes\":{\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"5990\",\"type\":\"Line\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"5965\",\"type\":\"Line\"},{\"attributes\":{\"overlay\":{\"id\":\"5986\",\"type\":\"BoxAnnotation\"}},\"id\":\"5953\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"5948\",\"type\":\"BasicTicker\"},{\"attributes\":{\"label\":{\"value\":\"persistence\"},\"renderers\":[{\"id\":\"5992\",\"type\":\"GlyphRenderer\"}]},\"id\":\"6015\",\"type\":\"LegendItem\"},{\"attributes\":{\"data_source\":{\"id\":\"6016\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"6017\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"6018\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"6020\",\"type\":\"CDSView\"}},\"id\":\"6019\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"AAD+1cOrdkIAADwfxKt2QgAAemjEq3ZCAAC4scSrdkIAAPb6xKt2QgAANETFq3ZCAAByjcWrdkIAALDWxat2QgAA7h/Gq3ZCAAAsacardkIAAGqyxqt2QgAAqPvGq3ZC\",\"dtype\":\"float64\",\"shape\":[12]},\"y\":{\"__ndarray__\":\"rygSOaregkCvKBI5qt6CQK8oEjmq3oJArygSOaregkCvKBI5qt6CQK8oEjmq3oJArygSOaregkCvKBI5qt6CQK8oEjmq3oJArygSOaregkCvKBI5qt6CQK8oEjmq3oJA\",\"dtype\":\"float64\",\"shape\":[12]}},\"selected\":{\"id\":\"6042\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"6043\",\"type\":\"UnionRenderers\"}},\"id\":\"5989\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"5991\",\"type\":\"Line\"},{\"attributes\":{\"data_source\":{\"id\":\"5989\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"5990\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"5991\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"5993\",\"type\":\"CDSView\"}},\"id\":\"5992\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null},\"id\":\"5934\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"6043\",\"type\":\"UnionRenderers\"}],\"root_ids\":[\"5933\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"5bb92f35-2777-4fdb-9368-0126b95d467f\",\"roots\":{\"5933\":\"1419dfb5-d044-4da6-98bb-e70b260be8a0\"}}];\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",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "5933"
      }
     },
     "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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"720d38da-75fb-4b13-8a99-2975ef8c7aac\" data-root-id=\"6506\"></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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\",\"dtype\":\"float64\",\"shape\":[1153]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1153]}},\"selected\":{\"id\":\"6586\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"6587\",\"type\":\"UnionRenderers\"}},\"id\":\"6536\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"6529\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"6546\",\"type\":\"DatetimeTickFormatter\"},{\"attributes\":{\"overlay\":{\"id\":\"6559\",\"type\":\"BoxAnnotation\"}},\"id\":\"6526\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"callback\":null},\"id\":\"6509\",\"type\":\"DataRange1d\"},{\"attributes\":{\"dimensions\":\"width\"},\"id\":\"6527\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"6511\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"6952\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"6587\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"source\":{\"id\":\"6536\",\"type\":\"ColumnDataSource\"}},\"id\":\"6540\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"6513\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"6586\",\"type\":\"Selection\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"6525\",\"type\":\"PanTool\"},{\"id\":\"6526\",\"type\":\"BoxZoomTool\"},{\"id\":\"6527\",\"type\":\"WheelZoomTool\"},{\"id\":\"6528\",\"type\":\"ResetTool\"},{\"id\":\"6529\",\"type\":\"SaveTool\"}]},\"id\":\"6530\",\"type\":\"Toolbar\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"6538\",\"type\":\"Line\"},{\"attributes\":{\"mantissas\":[1,2,5],\"max_interval\":500.0,\"num_minor_ticks\":0},\"id\":\"6547\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"6521\",\"type\":\"BasicTicker\"}},\"id\":\"6524\",\"type\":\"Grid\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"AADOs8qrdkIAAAz9yqt2QgAASkbLq3ZCAACIj8urdkIAAMbYy6t2QgAABCLMq3ZCAABCa8yrdkIAAIC0zKt2QgAAvv3Mq3ZCAAD8Rs2rdkIAADqQzat2QgAAeNnNq3ZC\",\"dtype\":\"float64\",\"shape\":[12]},\"y\":{\"__ndarray__\":\"JoYKQol3jUCwQmjI46qNQEEO1x7s2o1AhLoPMZwHjkDZ6opX7jCOQH11IVrdVo5AvctTb2R5jkBia848f5iOQO29adgptI5A51Vmx2DMjkBiBwIAIeGOQEo7Nuhn8o5A\",\"dtype\":\"float64\",\"shape\":[12]}},\"selected\":{\"id\":\"6952\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"6953\",\"type\":\"UnionRenderers\"}},\"id\":\"6589\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"max_interval\":1800000.0,\"min_interval\":1000.0,\"num_minor_ticks\":0},\"id\":\"6548\",\"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\":\"6550\",\"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\":\"6549\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]},\"id\":\"6554\",\"type\":\"MonthsTicker\"},{\"attributes\":{},\"id\":\"6528\",\"type\":\"ResetTool\"},{\"attributes\":{\"months\":[0,2,4,6,8,10]},\"id\":\"6555\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"days\":[1,4,7,10,13,16,19,22,25,28]},\"id\":\"6551\",\"type\":\"DaysTicker\"},{\"attributes\":{\"days\":[1,15]},\"id\":\"6553\",\"type\":\"DaysTicker\"},{\"attributes\":{\"line_color\":\"#ff7f0e\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"6590\",\"type\":\"Line\"},{\"attributes\":{\"months\":[0,6]},\"id\":\"6557\",\"type\":\"MonthsTicker\"},{\"attributes\":{},\"id\":\"6953\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"source\":{\"id\":\"6589\",\"type\":\"ColumnDataSource\"}},\"id\":\"6593\",\"type\":\"CDSView\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"6552\",\"type\":\"DaysTicker\"},{\"attributes\":{\"callback\":null},\"id\":\"6507\",\"type\":\"DataRange1d\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"6591\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"6558\",\"type\":\"YearsTicker\"},{\"attributes\":{\"label\":{\"value\":\"observed\"},\"renderers\":[{\"id\":\"6539\",\"type\":\"GlyphRenderer\"}]},\"id\":\"6561\",\"type\":\"LegendItem\"},{\"attributes\":{\"label\":{\"value\":\"persistence index\"},\"renderers\":[{\"id\":\"6592\",\"type\":\"GlyphRenderer\"}]},\"id\":\"6617\",\"type\":\"LegendItem\"},{\"attributes\":{\"axis_label\":\"Time (UTC)\",\"formatter\":{\"id\":\"6546\",\"type\":\"DatetimeTickFormatter\"},\"ticker\":{\"id\":\"6516\",\"type\":\"DatetimeTicker\"}},\"id\":\"6515\",\"type\":\"DatetimeAxis\"},{\"attributes\":{\"months\":[0,4,8]},\"id\":\"6556\",\"type\":\"MonthsTicker\"},{\"attributes\":{},\"id\":\"6521\",\"type\":\"BasicTicker\"},{\"attributes\":{\"num_minor_ticks\":5,\"tickers\":[{\"id\":\"6547\",\"type\":\"AdaptiveTicker\"},{\"id\":\"6548\",\"type\":\"AdaptiveTicker\"},{\"id\":\"6549\",\"type\":\"AdaptiveTicker\"},{\"id\":\"6550\",\"type\":\"DaysTicker\"},{\"id\":\"6551\",\"type\":\"DaysTicker\"},{\"id\":\"6552\",\"type\":\"DaysTicker\"},{\"id\":\"6553\",\"type\":\"DaysTicker\"},{\"id\":\"6554\",\"type\":\"MonthsTicker\"},{\"id\":\"6555\",\"type\":\"MonthsTicker\"},{\"id\":\"6556\",\"type\":\"MonthsTicker\"},{\"id\":\"6557\",\"type\":\"MonthsTicker\"},{\"id\":\"6558\",\"type\":\"YearsTicker\"}]},\"id\":\"6516\",\"type\":\"DatetimeTicker\"},{\"attributes\":{\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"6537\",\"type\":\"Line\"},{\"attributes\":{\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"6563\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"6525\",\"type\":\"PanTool\"},{\"attributes\":{\"text\":\"\"},\"id\":\"6541\",\"type\":\"Title\"},{\"attributes\":{\"data_source\":{\"id\":\"6589\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"6590\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"6591\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"6593\",\"type\":\"CDSView\"}},\"id\":\"6592\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"ticker\":{\"id\":\"6516\",\"type\":\"DatetimeTicker\"}},\"id\":\"6519\",\"type\":\"Grid\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"AADOs8qrdkIAAAz9yqt2QgAASkbLq3ZCAACIj8urdkIAAMbYy6t2QgAABCLMq3ZCAABCa8yrdkIAAIC0zKt2QgAAvv3Mq3ZCAAD8Rs2rdkIAADqQzat2QgAAeNnNq3ZC\",\"dtype\":\"float64\",\"shape\":[12]},\"y\":{\"__ndarray__\":\"C0P3v7a4i0ALQ/e/triLQAtD97+2uItAC0P3v7a4i0ALQ/e/triLQAtD97+2uItAC0P3v7a4i0ALQ/e/triLQAtD97+2uItAC0P3v7a4i0ALQ/e/triLQAtD97+2uItA\",\"dtype\":\"float64\",\"shape\":[12]}},\"selected\":{\"id\":\"6615\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"6616\",\"type\":\"UnionRenderers\"}},\"id\":\"6562\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"source\":{\"id\":\"6562\",\"type\":\"ColumnDataSource\"}},\"id\":\"6566\",\"type\":\"CDSView\"},{\"attributes\":{\"data_source\":{\"id\":\"6562\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"6563\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"6564\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"6566\",\"type\":\"CDSView\"}},\"id\":\"6565\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"6564\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"6616\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"label\":{\"value\":\"persistence\"},\"renderers\":[{\"id\":\"6565\",\"type\":\"GlyphRenderer\"}]},\"id\":\"6588\",\"type\":\"LegendItem\"},{\"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\":\"6559\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"items\":[{\"id\":\"6561\",\"type\":\"LegendItem\"},{\"id\":\"6588\",\"type\":\"LegendItem\"},{\"id\":\"6617\",\"type\":\"LegendItem\"}],\"location\":\"top_left\"},\"id\":\"6560\",\"type\":\"Legend\"},{\"attributes\":{},\"id\":\"6615\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"6544\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"data_source\":{\"id\":\"6536\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"6537\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"6538\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"6540\",\"type\":\"CDSView\"}},\"id\":\"6539\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"axis_label\":\"Irradiance (W/m^2)\",\"formatter\":{\"id\":\"6544\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"6521\",\"type\":\"BasicTicker\"}},\"id\":\"6520\",\"type\":\"LinearAxis\"}],\"root_ids\":[\"6506\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"be9ca815-8510-4969-a307-1596dec622f7\",\"roots\":{\"6506\":\"720d38da-75fb-4b13-8a99-2975ef8c7aac\"}}];\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",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
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       "id": "6506"
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     "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": [
    "And finally day ahead persistence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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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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