Tutorial 1 - TMY Weather Data.ipynb

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    "![tutorialpromo](images/tutorial_banner.PNG)\n",
    "\n",
    "\n",
    "# Tutorial 1 - Weather Data: Accesing it, understanding it, visualizing it!\n"
   ]
  },
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   "metadata": {
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     "slide_type": "subslide"
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    "This notebook explores a standard type of weather data, the typical meteorological year (TMY), and how to summarize it with Python and [Pandas](https://pandas.pydata.org/).\n",
    "\n",
    "![Overview](images/tutorial_1_overview.PNG)"
   ]
  },
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   "source": [
    "## Steps:\n",
    "- [Weather data in PV performance models](#Weather-Data-&-PV)\n",
    "- Looking at a sample weather data file\n",
    "- Where to get weather data from? \n",
    "- Weather data to API\n",
    "\n",
    "## PV Concepts:\n",
    "- TMY\n",
    "- GHI, DNI, DHI\n",
    "- DryBulb, Wspd\n",
    "- Irradiance vs. Insolation\n",
    "\n",
    "## Python Concepts:\n",
    "- Exploring a Pandas dataframe (`df`): `len()`, `df.head()`, `df.keys()`\n",
    "- [pvlib input-output tools](https://pvlib-python.readthedocs.io/en/stable/api.html#io-tools)\n",
    "- Ploting a Pandas dataframe (`df`): `df.plot()`\n",
    "- Aggregating data in a dataframe (`df`): [`df.resample(freq).sum()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.resample.html)\n",
    "- Pandas [`DateOffsets`](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) - shortcuts to set the frequency when resampling\n",
    "- Getting NREL irradiance data from the web based API using pvlib"
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    "## Weather Data & PV \n",
    "\n",
    "Weather and irradiance data are used as input to PV performance models.  \n",
    "\n",
    "\n",
    "![SRRL](images/tutorial_1_SRRL.PNG)\n",
    "\n",
    "\n",
    "\n",
    "These data are directly measured, derived from measured data, or simulated using a stochastic model.\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
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    "## Typical Meteorological Year\n",
    "\n",
    "TMY datasets are intended to represent the weather for a typical year at a given location. \n",
    "\n",
    "TMY datasets provide hourly **solar irradiance**, **air temperature**, **wind speed**, and other weather measurements for a hypothetical year that represents more or less a \"median year\" for solar resource.  \n",
    "\n",
    "\n",
    "![TMY3 screenshot](images/tutorial_1_tmy3_example.PNG)"
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    "TMY datasets are created by selecting individual months out of an extended period of weather measurememts (say, 20 years of data) to construct a single year's worth of data. There are several methods for selecting which months to include, but the general idea is to calculate monthly summary statistics and take the month that lies in the middle of the distribution.  For example, no two Januaries will be exactly the same, so summing the total solar irradiance for each January will give a [normal distribution](https://en.wikipedia.org/wiki/Normal_distribution), and the month that falls closest to the [median](https://en.wikipedia.org/wiki/Median) is chosen as the representative month. The same process is followed for February, March, and so on, and all twelve representative months are stitched together into a year-long dataset.  \n",
    "\n",
    "The oldest TMYs were calculated using data from the nearest weather station (airports and such). Today, it's common to use TMYs calculated using simulated weather data from satellite imagery because of the improved spatial resolution.\n",
    "\n",
    "To get a better feel for TMY data, we'll first explore an example TMY dataset that is bundled with pvlib."
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    "## First Step: Import Libraries\n",
    "\n",
    "In Python, some functions are builtin like `print()` but others must be imported before they can be used. For this notebook we're going to import three packages:\n",
    "* [pvlib](https://pvlib-python.readthedocs.io/en/stable/) - library for simulating performance of photovoltaic energy systems. \n",
    "* [pandas](https://pandas.pydata.org/) - analysis tool for timeseries and tabular data\n",
    "* [matplotlib](https://matplotlib.org/) - data visualization for Python\n",
    "\n",
    "Some Python modules are part of the [standard library](https://docs.python.org/3/library/index.html), but are not imported with builtins. We'll use the `pathlib` module which is useful for accessing files and folders."
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   "source": [
    "import os  # for getting environment variables\n",
    "import pathlib  # for finding the example dataset\n",
    "import pvlib\n",
    "import pandas as pd  # for data wrangling\n",
    "import matplotlib.pyplot as plt  # for visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Query which version you are using of pvlib:"
   ]
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   "execution_count": 2,
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8.0\n"
     ]
    }
   ],
   "source": [
    "print(pvlib.__version__)"
   ]
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    "## Reading a TMY dataset with pvlib\n",
    "\n",
    "First, we'll read the TMY dataset with [`pvlib.iotools.read_tmy3()`](https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.iotools.read_tmy3.html) which returns a [Pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) of the timeseries weather data and a second output with a Python dictionary of the TMY metadata like longitude, latitude, elevation, etc.\n",
    "\n",
    "We will use the Python [`pathlib`](https://docs.python.org/3/library/pathlib.html) to get the path to the `'data'` directory which comes with the `pvlib` package. Then we can use the slash operator, `/` to make the full path to the TMY file."
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     "text": [
      "Help on function read_tmy3 in module pvlib.iotools.tmy:\n",
      "\n",
      "read_tmy3(filename, coerce_year=None, recolumn=True)\n",
      "    Read a TMY3 file in to a pandas dataframe.\n",
      "    \n",
      "    Note that values contained in the metadata dictionary are unchanged\n",
      "    from the TMY3 file (i.e. units are retained). In the case of any\n",
      "    discrepancies between this documentation and the TMY3 User's Manual\n",
      "    [1]_, the TMY3 User's Manual takes precedence.\n",
      "    \n",
      "    The TMY3 files were updated in Jan. 2015. This function requires the\n",
      "    use of the updated files.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    filename : str\n",
      "        A relative file path or absolute file path.\n",
      "    \n",
      "    coerce_year : None or int, default None\n",
      "        If supplied, the year of the index will be set to `coerce_year`, except\n",
      "        for the last index value which will be set to the *next* year so that\n",
      "        the index increases monotonically.\n",
      "    \n",
      "    recolumn : bool, default True\n",
      "        If ``True``, apply standard names to TMY3 columns. Typically this\n",
      "        results in stripping the units from the column name.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    Tuple of the form (data, metadata).\n",
      "    \n",
      "    data : DataFrame\n",
      "        A pandas dataframe with the columns described in the table\n",
      "        below. For more detailed descriptions of each component, please\n",
      "        consult the TMY3 User's Manual ([1]), especially tables 1-1\n",
      "        through 1-6.\n",
      "    \n",
      "    metadata : dict\n",
      "        The site metadata available in the file.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    The returned structures have the following fields.\n",
      "    \n",
      "    ===============   ======  ===================\n",
      "    key               format  description\n",
      "    ===============   ======  ===================\n",
      "    altitude          Float   site elevation\n",
      "    latitude          Float   site latitudeitude\n",
      "    longitude         Float   site longitudeitude\n",
      "    Name              String  site name\n",
      "    State             String  state\n",
      "    TZ                Float   UTC offset\n",
      "    USAF              Int     USAF identifier\n",
      "    ===============   ======  ===================\n",
      "    \n",
      "    =============================       ======================================================================================================================================================\n",
      "    TMYData field                       description\n",
      "    =============================       ======================================================================================================================================================\n",
      "    TMYData.Index                       A pandas datetime index. NOTE, the index is currently timezone unaware, and times are set to local standard time (daylight savings is not included)\n",
      "    TMYData.ETR                         Extraterrestrial horizontal radiation recv'd during 60 minutes prior to timestamp, Wh/m^2\n",
      "    TMYData.ETRN                        Extraterrestrial normal radiation recv'd during 60 minutes prior to timestamp, Wh/m^2\n",
      "    TMYData.GHI                         Direct and diffuse horizontal radiation recv'd during 60 minutes prior to timestamp, Wh/m^2\n",
      "    TMYData.GHISource                   See [1]_, Table 1-4\n",
      "    TMYData.GHIUncertainty              Uncertainty based on random and bias error estimates                        see [2]_\n",
      "    TMYData.DNI                         Amount of direct normal radiation (modeled) recv'd during 60 mintues prior to timestamp, Wh/m^2\n",
      "    TMYData.DNISource                   See [1]_, Table 1-4\n",
      "    TMYData.DNIUncertainty              Uncertainty based on random and bias error estimates                        see [2]_\n",
      "    TMYData.DHI                         Amount of diffuse horizontal radiation recv'd during 60 minutes prior to timestamp, Wh/m^2\n",
      "    TMYData.DHISource                   See [1]_, Table 1-4\n",
      "    TMYData.DHIUncertainty              Uncertainty based on random and bias error estimates                        see [2]_\n",
      "    TMYData.GHillum                     Avg. total horizontal illuminance recv'd during the 60 minutes prior to timestamp, lx\n",
      "    TMYData.GHillumSource               See [1]_, Table 1-4\n",
      "    TMYData.GHillumUncertainty          Uncertainty based on random and bias error estimates                        see [2]_\n",
      "    TMYData.DNillum                     Avg. direct normal illuminance recv'd during the 60 minutes prior to timestamp, lx\n",
      "    TMYData.DNillumSource               See [1]_, Table 1-4\n",
      "    TMYData.DNillumUncertainty          Uncertainty based on random and bias error estimates                        see [2]_\n",
      "    TMYData.DHillum                     Avg. horizontal diffuse illuminance recv'd during the 60 minutes prior to timestamp, lx\n",
      "    TMYData.DHillumSource               See [1]_, Table 1-4\n",
      "    TMYData.DHillumUncertainty          Uncertainty based on random and bias error estimates                        see [2]_\n",
      "    TMYData.Zenithlum                   Avg. luminance at the sky's zenith during the 60 minutes prior to timestamp, cd/m^2\n",
      "    TMYData.ZenithlumSource             See [1]_, Table 1-4\n",
      "    TMYData.ZenithlumUncertainty        Uncertainty based on random and bias error estimates                        see [1]_ section 2.10\n",
      "    TMYData.TotCld                      Amount of sky dome covered by clouds or obscuring phenonema at time stamp, tenths of sky\n",
      "    TMYData.TotCldSource                See [1]_, Table 1-5, 8760x1 cell array of strings\n",
      "    TMYData.TotCldUncertainty           See [1]_, Table 1-6\n",
      "    TMYData.OpqCld                      Amount of sky dome covered by clouds or obscuring phenonema that prevent observing the sky at time stamp, tenths of sky\n",
      "    TMYData.OpqCldSource                See [1]_, Table 1-5, 8760x1 cell array of strings\n",
      "    TMYData.OpqCldUncertainty           See [1]_, Table 1-6\n",
      "    TMYData.DryBulb                     Dry bulb temperature at the time indicated, deg C\n",
      "    TMYData.DryBulbSource               See [1]_, Table 1-5, 8760x1 cell array of strings\n",
      "    TMYData.DryBulbUncertainty          See [1]_, Table 1-6\n",
      "    TMYData.DewPoint                    Dew-point temperature at the time indicated, deg C\n",
      "    TMYData.DewPointSource              See [1]_, Table 1-5, 8760x1 cell array of strings\n",
      "    TMYData.DewPointUncertainty         See [1]_, Table 1-6\n",
      "    TMYData.RHum                        Relatitudeive humidity at the time indicated, percent\n",
      "    TMYData.RHumSource                  See [1]_, Table 1-5, 8760x1 cell array of strings\n",
      "    TMYData.RHumUncertainty             See [1]_, Table 1-6\n",
      "    TMYData.Pressure                    Station pressure at the time indicated, 1 mbar\n",
      "    TMYData.PressureSource              See [1]_, Table 1-5, 8760x1 cell array of strings\n",
      "    TMYData.PressureUncertainty         See [1]_, Table 1-6\n",
      "    TMYData.Wdir                        Wind direction at time indicated, degrees from north (360 = north; 0 = undefined,calm)\n",
      "    TMYData.WdirSource                  See [1]_, Table 1-5, 8760x1 cell array of strings\n",
      "    TMYData.WdirUncertainty             See [1]_, Table 1-6\n",
      "    TMYData.Wspd                        Wind speed at the time indicated, meter/second\n",
      "    TMYData.WspdSource                  See [1]_, Table 1-5, 8760x1 cell array of strings\n",
      "    TMYData.WspdUncertainty             See [1]_, Table 1-6\n",
      "    TMYData.Hvis                        Distance to discernable remote objects at time indicated (7777=unlimited), meter\n",
      "    TMYData.HvisSource                  See [1]_, Table 1-5, 8760x1 cell array of strings\n",
      "    TMYData.HvisUncertainty             See [1]_, Table 1-6\n",
      "    TMYData.CeilHgt                     Height of cloud base above local terrain (7777=unlimited), meter\n",
      "    TMYData.CeilHgtSource               See [1]_, Table 1-5, 8760x1 cell array of strings\n",
      "    TMYData.CeilHgtUncertainty          See [1]_, Table 1-6\n",
      "    TMYData.Pwat                        Total precipitable water contained in a column of unit cross section from earth to top of atmosphere, cm\n",
      "    TMYData.PwatSource                  See [1]_, Table 1-5, 8760x1 cell array of strings\n",
      "    TMYData.PwatUncertainty             See [1]_, Table 1-6\n",
      "    TMYData.AOD                         The broadband aerosol optical depth per unit of air mass due to extinction by aerosol component of atmosphere, unitless\n",
      "    TMYData.AODSource                   See [1]_, Table 1-5, 8760x1 cell array of strings\n",
      "    TMYData.AODUncertainty              See [1]_, Table 1-6\n",
      "    TMYData.Alb                         The ratio of reflected solar irradiance to global horizontal irradiance, unitless\n",
      "    TMYData.AlbSource                   See [1]_, Table 1-5, 8760x1 cell array of strings\n",
      "    TMYData.AlbUncertainty              See [1]_, Table 1-6\n",
      "    TMYData.Lprecipdepth                The amount of liquid precipitation observed at indicated time for the period indicated in the liquid precipitation quantity field, millimeter\n",
      "    TMYData.Lprecipquantity             The period of accumulatitudeion for the liquid precipitation depth field, hour\n",
      "    TMYData.LprecipSource               See [1]_, Table 1-5, 8760x1 cell array of strings\n",
      "    TMYData.LprecipUncertainty          See [1]_, Table 1-6\n",
      "    TMYData.PresWth                     Present weather code, see [2]_.\n",
      "    TMYData.PresWthSource               Present weather code source, see [2]_.\n",
      "    TMYData.PresWthUncertainty          Present weather code uncertainty, see [2]_.\n",
      "    =============================       ======================================================================================================================================================\n",
      "    \n",
      "    .. warning:: TMY3 irradiance data corresponds to the *previous* hour, so\n",
      "        the first index is 1AM, corresponding to the irradiance from midnight\n",
      "        to 1AM, and the last index is midnight of the *next* year. For example,\n",
      "        if the last index in the TMY3 file was 1988-12-31 24:00:00 this becomes\n",
      "        1989-01-01 00:00:00 after calling :func:`~pvlib.iotools.read_tmy3`.\n",
      "    \n",
      "    .. warning:: When coercing the year, the last index in the dataframe will\n",
      "        become midnight of the *next* year. For example, if the last index in\n",
      "        the TMY3 was 1988-12-31 24:00:00, and year is coerced to 1990 then this\n",
      "        becomes 1991-01-01 00:00:00.\n",
      "    \n",
      "    References\n",
      "    ----------\n",
      "    \n",
      "    .. [1] Wilcox, S and Marion, W. \"Users Manual for TMY3 Data Sets\".\n",
      "       NREL/TP-581-43156, Revised May 2008.\n",
      "    \n",
      "    .. [2] Wilcox, S. (2007). National Solar Radiation Database 1991 2005\n",
      "       Update: Users Manual. 472 pp.; NREL Report No. TP-581-41364.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pvlib.iotools.read_tmy3)"
   ]
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    {
     "data": {
      "text/plain": [
       "{'USAF': 723170,\n",
       " 'Name': '\"GREENSBORO PIEDMONT TRIAD INT\"',\n",
       " 'State': 'NC',\n",
       " 'TZ': -5.0,\n",
       " 'latitude': 36.1,\n",
       " 'longitude': -79.95,\n",
       " 'altitude': 273.0}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA_DIR = pathlib.Path(pvlib.__file__).parent / 'data'\n",
    "df_tmy, meta_dict = pvlib.iotools.read_tmy3(DATA_DIR / '723170TYA.CSV', coerce_year=1990)\n",
    "meta_dict  # display the dictionary of metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's display the first 4 lines of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date (MM/DD/YYYY)</th>\n",
       "      <th>Time (HH:MM)</th>\n",
       "      <th>ETR</th>\n",
       "      <th>ETRN</th>\n",
       "      <th>GHI</th>\n",
       "      <th>GHISource</th>\n",
       "      <th>GHIUncertainty</th>\n",
       "      <th>DNI</th>\n",
       "      <th>DNISource</th>\n",
       "      <th>DNIUncertainty</th>\n",
       "      <th>...</th>\n",
       "      <th>Alb</th>\n",
       "      <th>AlbSource</th>\n",
       "      <th>AlbUncertainty</th>\n",
       "      <th>Lprecipdepth</th>\n",
       "      <th>Lprecipquantity</th>\n",
       "      <th>LprecipSource</th>\n",
       "      <th>LprecipUncertainty</th>\n",
       "      <th>PresWth</th>\n",
       "      <th>PresWthSource</th>\n",
       "      <th>PresWthUncertainty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01 01:00:00-05:00</th>\n",
       "      <td>01/01/1988</td>\n",
       "      <td>01:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 02:00:00-05:00</th>\n",
       "      <td>01/01/1988</td>\n",
       "      <td>02:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 03:00:00-05:00</th>\n",
       "      <td>01/01/1988</td>\n",
       "      <td>03:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 04:00:00-05:00</th>\n",
       "      <td>01/01/1988</td>\n",
       "      <td>04:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Date (MM/DD/YYYY) Time (HH:MM)  ETR  ETRN  GHI  \\\n",
       "1990-01-01 01:00:00-05:00        01/01/1988        01:00    0     0    0   \n",
       "1990-01-01 02:00:00-05:00        01/01/1988        02:00    0     0    0   \n",
       "1990-01-01 03:00:00-05:00        01/01/1988        03:00    0     0    0   \n",
       "1990-01-01 04:00:00-05:00        01/01/1988        04:00    0     0    0   \n",
       "\n",
       "                           GHISource  GHIUncertainty  DNI  DNISource  \\\n",
       "1990-01-01 01:00:00-05:00          1               0    0          1   \n",
       "1990-01-01 02:00:00-05:00          1               0    0          1   \n",
       "1990-01-01 03:00:00-05:00          1               0    0          1   \n",
       "1990-01-01 04:00:00-05:00          1               0    0          1   \n",
       "\n",
       "                           DNIUncertainty  ...  Alb  AlbSource  \\\n",
       "1990-01-01 01:00:00-05:00               0  ...  0.0          ?   \n",
       "1990-01-01 02:00:00-05:00               0  ...  0.0          ?   \n",
       "1990-01-01 03:00:00-05:00               0  ...  0.0          ?   \n",
       "1990-01-01 04:00:00-05:00               0  ...  0.0          ?   \n",
       "\n",
       "                           AlbUncertainty  Lprecipdepth  Lprecipquantity  \\\n",
       "1990-01-01 01:00:00-05:00               0             0                1   \n",
       "1990-01-01 02:00:00-05:00               0             0                1   \n",
       "1990-01-01 03:00:00-05:00               0             0                1   \n",
       "1990-01-01 04:00:00-05:00               0             0                1   \n",
       "\n",
       "                           LprecipSource  LprecipUncertainty  PresWth  \\\n",
       "1990-01-01 01:00:00-05:00              D                   9        0   \n",
       "1990-01-01 02:00:00-05:00              D                   9        0   \n",
       "1990-01-01 03:00:00-05:00              D                   9        0   \n",
       "1990-01-01 04:00:00-05:00              D                   9        0   \n",
       "\n",
       "                           PresWthSource  PresWthUncertainty  \n",
       "1990-01-01 01:00:00-05:00              C                   8  \n",
       "1990-01-01 02:00:00-05:00              C                   8  \n",
       "1990-01-01 03:00:00-05:00              C                   8  \n",
       "1990-01-01 04:00:00-05:00              C                   8  \n",
       "\n",
       "[4 rows x 71 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmy.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "This dataset follows the standard format of handling timeseries data with pandas -- one row per timestamp, one column per measurement type.  Because TMY files represent one year of data (no leap years), that means they'll have 8760 rows.  The number of columns can vary depending on the source of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 8760\n",
      "Number of columns: 71\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of rows:\", len(df_tmy))\n",
    "print(\"Number of columns:\", len(df_tmy.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can access single rows by pointing to its number location (iloc) or by using the index name it has. In this case, that is a dateTime\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tmy.iloc[0];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tmy.loc['1990-01-01 01:00:00-05:00'];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also print all the column names in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date (MM/DD/YYYY)', 'Time (HH:MM)', 'ETR', 'ETRN', 'GHI', 'GHISource',\n",
       "       'GHIUncertainty', 'DNI', 'DNISource', 'DNIUncertainty', 'DHI',\n",
       "       'DHISource', 'DHIUncertainty', 'GHillum', 'GHillumSource',\n",
       "       'GHillumUncertainty', 'DNillum', 'DNillumSource', 'DNillumUncertainty',\n",
       "       'DHillum', 'DHillumSource', 'DHillumUncertainty', 'Zenithlum',\n",
       "       'ZenithlumSource', 'ZenithlumUncertainty', 'TotCld', 'TotCldSource',\n",
       "       'TotCldUncertainty', 'OpqCld', 'OpqCldSource', 'OpqCldUncertainty',\n",
       "       'DryBulb', 'DryBulbSource', 'DryBulbUncertainty', 'DewPoint',\n",
       "       'DewPointSource', 'DewPointUncertainty', 'RHum', 'RHumSource',\n",
       "       'RHumUncertainty', 'Pressure', 'PressureSource', 'PressureUncertainty',\n",
       "       'Wdir', 'WdirSource', 'WdirUncertainty', 'Wspd', 'WspdSource',\n",
       "       'WspdUncertainty', 'Hvis', 'HvisSource', 'HvisUncertainty', 'CeilHgt',\n",
       "       'CeilHgtSource', 'CeilHgtUncertainty', 'Pwat', 'PwatSource',\n",
       "       'PwatUncertainty', 'AOD', 'AODSource', 'AODUncertainty', 'Alb',\n",
       "       'AlbSource', 'AlbUncertainty', 'Lprecipdepth', 'Lprecipquantity',\n",
       "       'LprecipSource', 'LprecipUncertainty', 'PresWth', 'PresWthSource',\n",
       "       'PresWthUncertainty'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmy.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "There are 71 columns, which is quite a lot!  For now, let's focus just on the ones that are most important for PV modeling -- the irradiance, temperature, and wind speed columns, and extract them into a new DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Irradiance \n",
    "\n",
    "Irradiance is an instantaneous measurement of solar power over some area.  For practical purposes of measurement and interpretation, irradiance is expressed and separated into different components.\n",
    "\n",
    "![overview irradiance](images/tutorial_1_DNIDHIGHI.PNG)\n",
    "\n",
    "The units of irradiance are watts per square meter.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Wind\n",
    "\n",
    "Wind speed is measured with an anemometer.  The most common type is a the cup-type anemometer, shown on the right side of the picture below.  The number of rotations per time interval is used to calculate the wind speed.  The vane on the left is used to measure the direction of the wind.  Wind direction is reported as the direction from which the wind is blowing.\n",
    "\n",
    "<img src=\"https://pvpmc.sandia.gov/wp-content/uploads/2012/04/anemometer.jpg\"></img>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Air temperature \n",
    "\n",
    "Also known as dry-bulb temperature, is the temperature of the ambient air when the measurement device is shielded from radiation and moisture. The most common method of air temperature measurement uses a resistive temperature device (RTD) or thermocouple within a radiation shield. The shield blocks sunlight from reaching the sensor (avoiding radiative heating), yet allows natural air flow around the sensor. More accurate temperature measurement devices utilize a shield which forces air across the sensor.\n",
    "\n",
    "Air temperature is typically measured on the Celsius scale.\n",
    "\n",
    "Air temperature plays a large role in PV system performance as PV modules and inverters are cooled convectively by the surrounding air.\n",
    "\n",
    "<img src=\"https://pvpmc.sandia.gov/wp-content/uploads/2012/04/AmbTemp.jpg\" width=\"400\" height=\"400\"> </img>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Downselect columns \n",
    "\n",
    "There are a lot more weather data in that file that you can access. To investigate all the column headers, we used `.keys()` above. Always read the [Instruction Manual](https://www.nrel.gov/docs/fy08osti/43156.pdf) for the weather files to get more details on how the data is aggregated, units, etc.\n",
    "\n",
    "At this point we are interested in <b> GHI, DHI, DNI, DryBulb </b> and <b> Wind Speed </b>. For this NREL TMY3 dataset the units of irradiance are W/m&sup2;, dry bulb temperature is in &deg;C, and wind speed is m/s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>GHI</th>\n",
       "      <th>DHI</th>\n",
       "      <th>DNI</th>\n",
       "      <th>DryBulb</th>\n",
       "      <th>Wspd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01 01:00:00-05:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 02:00:00-05:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 03:00:00-05:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 04:00:00-05:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 05:00:00-05:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 06:00:00-05:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 07:00:00-05:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 08:00:00-05:00</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 09:00:00-05:00</th>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 10:00:00-05:00</th>\n",
       "      <td>79</td>\n",
       "      <td>78</td>\n",
       "      <td>4</td>\n",
       "      <td>10.6</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 11:00:00-05:00</th>\n",
       "      <td>199</td>\n",
       "      <td>198</td>\n",
       "      <td>3</td>\n",
       "      <td>11.7</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 12:00:00-05:00</th>\n",
       "      <td>261</td>\n",
       "      <td>260</td>\n",
       "      <td>3</td>\n",
       "      <td>11.7</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 13:00:00-05:00</th>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 14:00:00-05:00</th>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>2</td>\n",
       "      <td>11.7</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 15:00:00-05:00</th>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           GHI  DHI  DNI  DryBulb  Wspd\n",
       "1990-01-01 01:00:00-05:00    0    0    0     10.0   6.2\n",
       "1990-01-01 02:00:00-05:00    0    0    0     10.0   5.2\n",
       "1990-01-01 03:00:00-05:00    0    0    0     10.0   5.7\n",
       "1990-01-01 04:00:00-05:00    0    0    0     10.0   5.7\n",
       "1990-01-01 05:00:00-05:00    0    0    0     10.0   5.2\n",
       "1990-01-01 06:00:00-05:00    0    0    0     10.0   4.1\n",
       "1990-01-01 07:00:00-05:00    0    0    0     10.0   4.1\n",
       "1990-01-01 08:00:00-05:00    9    9    1     10.0   5.2\n",
       "1990-01-01 09:00:00-05:00   46   46    3     10.0   5.2\n",
       "1990-01-01 10:00:00-05:00   79   78    4     10.6   5.2\n",
       "1990-01-01 11:00:00-05:00  199  198    3     11.7   6.2\n",
       "1990-01-01 12:00:00-05:00  261  260    3     11.7   5.2\n",
       "1990-01-01 13:00:00-05:00  155  155    0     11.7   5.2\n",
       "1990-01-01 14:00:00-05:00  144  144    2     11.7   3.1\n",
       "1990-01-01 15:00:00-05:00  131  131    1     11.1   4.1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GHI, DHI, DNI are irradiance measurements\n",
    "# DryBulb is the \"dry-bulb\" (ambient) temperature\n",
    "# Wspd is wind speed\n",
    "df = df_tmy[['GHI', 'DHI', 'DNI', 'DryBulb', 'Wspd']]\n",
    "# show the first 15 rows:\n",
    "df.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting time series data with pandas and matplotlib\n",
    "\n",
    "Let's make some plots to get a better idea of what TMY data gives us.\n",
    "\n",
    "### Irradiance\n",
    "\n",
    "First, the three irradiance fields:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_week = df.head(24*7)  # Plotting 7 days, each one has 24 hours or entries\n",
    "first_week[['GHI', 'DHI', 'DNI']].plot()\n",
    "plt.ylabel('Irradiance [W/m$^2$]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's control the parameters a bit more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oCxFpRxq3r4wxeS7TiaJzZ2jWzGoUIeH3l/z/AO+LyNPe6/OwXk/GND6RiOvK1O7bKyGrugXqdu/ev0jnzj7OX1DgJgS0RBEKvmoUqvo33DrZa73Hud4+Y0xjEom40dOFhd/a/dZbLkk8+CDU1EDbtnDVVS6BfP21z2vYWIrQ8NuYXQQcDRwItAN+GJ33yRjTiMQZbKcKd9zhVjK96ipX4Sgvhw8+SPMaNpYiNPy2UbwIjASqge0xD2NMY7Ju3X6JYvp0ePdduO02aN7c7Ssvh08/he3p/JYoLobVq+PfxzJZ5beNopuqjshIJMaY/BGJQP/+e19GaxPFxXDFFfsOKy93t6A++ghOOMHnNYqLXeHVq/eNqzA54bdG8Z6I2OgXYxq7Wreepk2D2bPh9tuhqGjfYYMHu21at59iB92ZnPJboxgKXCYiy4Eq3DQeqqoDAo/MGBNOe/bAxo17E0W0NtGrF1x22bcP7dzZVQw+/DCN69igu9Dwmyi+l5EojDH5Y8MGlx28RPHyyzBnDowf77rG1pZ2g3Z0vWxr0M45v91jVwJbgM5Aj5iHMaaxiBlsF61NHHooXHxx/MPLy2H58n3FUnbAAW6pVatR5JyvGoWIXAXcAHQD5gHfAd4HTg48MmNMOMUkihdegHnzYMKE+LUJcIkC3O2nM87wea0ePSxRhIDfxuwbgMHASlU9CTfPk9+/E4wx+cxLFDXtO3LXXXD44XDhhYkPHzTIDbRO6/aTDboLBb+JolJVK8ENvvOmHO8bfFjGmNDyEsVzH3Zn/nx366lJknsTrVpBaWk9GrRXrnRtIiZn/CaKCm+a8ReA6SLyIrA66KCMMSEWibCHAu584CD69YPRo+suMniwq1H4/n1fXAzbtrnV9EzO+G3MPkdVN6nqXcD/AcYDo/ycQ0S6i8hbIrJQRBaIyA3e/nYiMl1Elnjb2FlqbxWRpSKyWERO93M9Y0zAIhGePuAyPlso3HXXftM9xVVe7hbFW7HC57VsLEUo+K1R7KWqb6vqVFXd5bNoNfCfqlqCawy/XkRKgVuAN1S1D/CG9xrvvdFAf2AE8JCIpPDRNMZkwp6167lr96/p3x/OOy+1MtEGbd/tFDaWIhRSShQi8o633SoiW2pv/VxQVdeo6kfe863AQuAQ3BxSE7zDJrCvpjISmKyqVaq6HFgKlPu5pjEmOH9fMIDFu3tz992ukToVRx7pRmxboshPKXWPVdWh3rZ1kBcXkZ64nlOzgc6qusa7zhoR6eQddggwK6ZYhbev9rnGAGMAiqMfLmNMoKqr4TdfXMiAA5dzzjm9Ui7XtCkMHJhGg3bHji7D2KC7nEopUYjIL5O9r6q/93thEWkFPAv8QlW3iEjCQ+NdMk4M44BxAGVlZdZFwpgMmDQJluzqyfMn/pmCgut9lR08GB57zCWbZL2kvsUWMAqFVNsoWnuPMuAnuL/oDwGuBUr9XlREmuKSxCRVfc7bvVZEunrvdwXWefsrgO4xxbthPa2Mybrdu+E3v1GO4SNGln3lu3x5OezYAZ995rOgDbrLuZQSharerap3Ax2Agar6n6r6n8Ag3C/ulImrOowHFtaqiUwFLvWeX4pb+yK6f7SIFIlIL6APkO5SKMaYNP3tb7BsmfAb7kA6+V8ru14N2pYocspvr6diILaX0y6gp89zHA9cDJwsIvO8xxnAfcCpIrIEONV7jaouAKYAnwHTgOtVdY/Paxpj0tCli1upTsStWgfwfV6my93X+j7XYYfBQQelmShWr4ZdfjtYmqD4nT12IvCBiDyPayc4B/C1ZraqvkP8dgeA4QnKjAXG+rmOMab+1q5NsH9zC9/nSntp1OJiN1Lvq6/cXOYm6/wOuBsLXA58A2wCLlfVezMQlzGmAYoujbpjh49CNugu59IZcLccN2Psx0BrETkx2JCMMQ3V4MFu3aOPP/ZRyMZS5JyvROFNMz4TeA2429veFXxYxjQOsW0AsY8uXYIt45cq/OlPwZ0vKq2lUbt5/WUsUeSM3zaK6DTjs1T1JBHph0sYxpg0JGwD8PZX7qpmy/YqNm+rYuvOXWzZvou1a+MPKE10Lr+2b4cxY+DJJ4M5X6yuXd2wCF+JokUL6NTJBt3lkN9EUamqlSKyd5pxEbFpxo3JACnYA9oE9zVtmZVrLl0K557r2hH+67/gwQfjJ6DOndO/RtoN2lajyBm/iaL2NOPfYIPfjMmI4y/8F82buxksmhcJLVoILYoKeOzOoQnLzF6wmmP7H5zW9V56yS1nWlgI06bBaafBr39d66DTToPNm2H27LSuAS5RPPusW3q7ffsUC/XokcZIPROUlBOFN1Du56q6CbhLRN4C2uDGNhhjAvbOE8Pi7n/szsRlvnNUe44+622eeKCU/r1SGxS3Zw/cdZerQQwaBM88Az17Jjg4EoFD9ptqzZfYpVFHjEixUHGxy16qrkHGZFXKjdmqqriaRPR1utOMG2OAd+dXBH7OPsM+YN7UoRzRrwVDL5rByq83Jz1+wwY480yXJK64At55J0mSAJcoOvoflR1r0CD3u97X7afiYtd4snFjva5t0uO3e+wsERmckUiMaUQ2bavktO9vAWrivp+sDSDRe507w+f/PIFX3ltJ97L5vDtpGL16KadfM4NOnWvi9pTq1AneegvGjYPx46F58yRBqwaSKFq3hpKSNBIFWDtFjvhNFCcB74vIFyIyX0Q+EZH5mQjMmIbs2HM+ZMeXpdz20Ieost/j668Tl/366/2Pjy3zve/05st3j2Py9MV0LPmc18cNI7Iu/le9psbVIq6+OoWgt25102jUM1HAvgbtlJdGtUF3OeU3UXwPOBQ4Gfg+cJa3Ncak6JLbZ/L5P0/g+B/PYOxPjs3Ydc4/pS9rPyrnkec/SXrc4FTvEUQibhtQoohEfPR4tRpFTqWcKLzG7BpVXVn7kcH4jGlQJvzjMybedyztj5zDm385ISvXHDPqyGBOFHCiAB8LGXXo4O6LWaLIibQbs40x/ixcsZ4rL2pD4YER3v9Hb5o1DcfS77e/eTvrtq+r+8AAE8WRR0KzZj7aKURcrcIG3eWENWYbkwWVu6o5/swv2bOtPX99cit9urfLdUh73fuve+nxhx5c94/rWPbNssQHBpgomjWDY45Jo0HbahQ5kU5j9ixrzDbGn5MufYdvPhvIZbd9yEUjSrJ+/WQ9pRZev5CLjryI8R+Pp8+DfRj9zGg+WvPR/gcHmCjA3X6aM8ctjZoSW+kuZ/wmihFAb1xj9lnAmd7WGJPArx6cxazJw+h32kz+cnd22iVqS9ZTqm+Hvjx69qMsv2E5Nw65kVeXvsqgcYM4deKp/HPZP9Fo16RIxLUTtAxmOpHo0qgLF6ZYoLgY1qyBqqpArm9Sl9LIbBHZiluoaL+3vP0HBhmUMQ3Fq7OW8dubSmnZcwGzn8tcD6cgHNz6YO4/9X5uO+E2Hpn7CA/MeoBTJ57KwK4DGdV3FIW73+Ck/m0YEtDI6NgG7SNTaW+P9nyqqIBDDw0kBpOaVNfMbq2qB8Z5tFZVSxLGxPH1xm2cc04N0mQX0186iANbFuU6pJS0ad6Gm4+/mRU3rODR7z9KZHuEO2bcwe1t5zH8jHW8v+r9QK7je2lU6yKbM+ksXGSMqUNNjTL4zE+oWtuL+//flww5on7zI+VCUZMirhp4FdcMugYAFdhVoMxYMSOQ8xcUQFmZj0Rhg+5yxhKFMQGJXVCosFComDUEtJD/uWlgrkOrl5N7nUzzJs1B3YQjQ3sknr3Wr/JymD8fdu5M4WBbwChnLFEYE5C6FiHKV0O6D+HNS95k9MJCVGDWqlmBnbu83MfSqEVFLhtbosg6SxTGBKB6T/zJ/RqKIe2P4skpexhVUModM+5gyYYlgZzX9whtG3SXE5YojElTTY0y6bWFlP1wBs07JJnFryGIRBDgz52voKiwiKteuooarX9y7NrV3VHy1aBtNYqss0RhTC2xbQ2xjy5d3PuvzlrGsMtm0LzrCi4aUcLc54+nfc/g15YIFW+w3cFd+vD703/PzJUzGTd3XCCn9rU0anTQXcrTzpogWKIwppZkbQ0HFC/kjCG9eXvCibRst4kf3zaTz1dsZe3H5dkNMttiRmVffvTlnNL7FG6efjOrNq+q96kHD3Zrdae0JlFxsWv53rCh3tc1qbNEYYwPBYU1jPz5DD5cuJZvFh7DE2NP3DtvU7JpMvJeTKIQEcadNY49uodrXr5m38jtNPlqp4iOpbB2iqyyRGGMD9uW9+eFPw6jrF/X/d6ra0GhvFZrnqdebXvx38P/m1eXvsqkTybV69S+lka1QXc5YYnCGFO3SASaNoUD903EcP3g6xnSbQg3TLuBtdvS7wPcpg3065dijcIG3eWEJQpjTN2ia2XHzPNUWFDI+LPHs23XNn4+7ef1On3KS6O2awcHHGCJIsuynihE5HERWScin8bsayci00VkibdtG/PerSKyVEQWi8jp2Y7XNC41NUpBk11x32sQbQ3piiaKWko6lnDHiXcwZcEUXlj0QtqnLy93nQVW1dU2Hl3AyBJFVuWiRvFX3HTlsW4B3lDVPsAb3mtEpBQYDfT3yjwkIuFYFsw0SJff+Q411c0447oZDbOtIV0JEgXAzcffzFGdj+In//gJ3+z8Jq3TR9ftTrmdwhqzsyrriUJVZwK1O8KNBCZ4zycAo2L2T1bVKlVdDiwFGng/RJMrr85axt/uH0jb0o948U8n5jqccEmSKJoWNuXxkY8T2R7hxtdvTOv0Awb4WBrVahRZF5Y2is6qugbA23by9h8CxFZGK7x9xgRqy/YqfvCjKqRpJf98/mCaFIblqxESSRIFwMCuA7npuJt4fN7j/HPZP32fvqgIjj7aR4P22rVQWen7OiY9Yf82xFshJW5zl4iMEZE5IjInEu3KZ0yKhl/2PjtXlXDLb5cy8PAuuQ4nXKqqYMuWOpdAveO7d3B4+8O5+qWr2bZrm+/LRJdG3bOnjgNjFzAyWRGWRLFWRLoCeNt13v4KoHvMcd2A1fFOoKrjVLVMVcs6BrSmr2kc7pswlznPDKP/GTO59/pwr0KXE59/7rYHH5z0sBZNWzD+7PGs3LSS29+83fdlysth2zZYtKiOA23QXdaFJVFMBS71nl8KvBizf7SIFIlIL6APkOqsMMbUaeGK9fz6Z90o6rKUGU+W5TqccHrySSgshLPOqvPQocVDuX7w9fxp9p94b9V7KV+iSxe45BL3/Igj9p9f61ts0F3W5aJ77N+B94G+IlIhIlcC9wGnisgS4FTvNaq6AJgCfAZMA65X1boqpsakpKZGGXbuMmp2tuGJJ2vo0OaAXIcUPjU1MGkSnH46dOpU9/HAvcPvpXub7lz47IXc8/Y9KS2d6mstj27dXBaxRJE1uej1dIGqdlXVpqraTVXHq+oGVR2uqn287caY48eq6qGq2ldVX812vKbhGv2rmaz7uJxzfzabH550eK7DCae333aDGy6+OOUirYta84tjf8HKzSu5c8adDP/b8MDW2QZc96iuXS1RZFFYbj0Zk1XPv72Ep/9QTsejPuTp31lX2IQmToTWreHss30Vq6yuRBAUpWpPVWDrbO9lXWSzyhKFaXQ2btnJBReCNN/GW8/3pKAgXuc6w44d8Mwz8IMfuGkzfBjWc5hbZxuo0Rp6t+0dbGw26C6rLFGYRuekiz+ganUffvPHlfTvZT3kEpo6FbZu9XXbKWpI9yG8cckb3HzczRzY7EDumXlPWl1mE4rWKGwBo6ywRGEalbvGfcj8qd/lmFFvc/sV1sspqYkTXcPxsGFpFR/SfQj3n3o/z57/LAvXL+SqqVclXLvC91oePXq48R02ZiorLFGYBq32sqZ3X+MmFfrqPWuXSGrtWnjtNfjxj6Ggfr8mTul9CmNPHstTC57ij7P/GPeY2LU8amqgf383/1PC+bWsi2xWWaIwDVqibpfr1lm7RFKTJ7sh0mncdornV8f/ilH9RnHj6zcyc+XMpMeKwLXXuuk85s5NcJANussqSxTGmP1NnAjHHOP+tA+AiDBh1AQObXcoP3r6R6zeGneChb0uvti1nz/ySIIDrEaRVZYojDHftnCh+1M+oNpE1IFFB/Lcj55j265tnPf0eezaE3/dD3Cr3l1wgRsUvnlznAPatoVWrSxRZIklCtNgVe+pyXUI+emJJ1y7xAUXBH7q/p368/jIx3lv1Xt1Tkl+7bWwfbsbGL4fW8AoqyxRmAapclc1/U5Nfa4h46mpcYnitNMSTLRUfz/q/yN++Z1f8uAHD/LE/CcSHldWBgMHwsMPJ+gFa4kiayxRmAZn285d9Pnuh3zx1lCatbBlTX3517/cL9+AbzvVdt8p93FijxMZ89IY/v31vxMed+218MknMGtWnDdt0F3WWKIwDcqmbZUcevw8KmYN4ayfzqBqR7P9ljRt9MuaJjNxIrRsCSNHZvQyTQubMuWHU2jboi3nTjk34RKqF1zgZhB5+OE4b/bo4cZR7NyZ0ViNJQrTgKz7ZjuHfmcB6z4u54JbZvLSg8NyHVJ+2bkTnn7aTdnRsmXGL9e5VWeeOe8ZVm1exSUvXEKN7t+m1KqVq9w89RRsrL2AcrTn06pV+5UzwbJEYRqEisgW+hy7lI2fHc3V97zDk/9tA+p8e+klt5Jdhm87xRrSfQgPnP4AL3/+MmNnjo17zDXXuEHYEybUesO6yGaNJQqT97746hv6Dq5gyxel/MfvPmDc7UNzHVJ+mjjRrWJ30klZvex1g6/j4gEXc+eMO3l1yf4rCQwYAMcd58ZUfKtR2wbdZY0lCpPXPl0W4YjyCDu+6s3/eehjfv/LIbkOKT9FIjBtmpuyo7Awq5cWER4+62EGdB7A+c+cz02v37Tf+hXXXguLF7vlMfY65BDXjddqFBlnicLkjdrzNonAkYd2pHL1Ydz/+Gf85pryXIeYv556Cqqrs3rbKdYBTQ/g1yf8mq27tvK793+332JHP/yhG2P3rUbtpk1dDcgSRcZZojB5I9G8TVDAzRcPzGYoDc/EiXDUUXDkkTkLYenGpRR4v5J2Vu/krRVv7X2vRQu47DJ47rlanwMbS5EVliiMaewWL4YPPshZbSJqWM9hFDUp2pssFkYWfuv9a66B3bvhL3+J2WmJIissURjT2EWn7LjwwpyGEV3s6J6T72FU31E88ckT/HXeX/e+37eva2d/5BE3gBzYlyhqbLqWTLJEYUxjFp2y45RToGvXXEfDkO5DuO2E25hy3hSG9xrOmJfGfGta8muvhRUr4PXXvR09esCuXbBuXU7ibSwsUZi88OZc6wKZEe++637z5vi2U21NC5vy9HlP07ttb8596ly+2PgFAKNGQadOMdOP21iKrLBEYUJv5rxVnHZKEyD+7QWbt6keolN2nHNOriPZT9sWbXn5wpdRlO///ftsqtxEs2Zw5ZVubGBFBZYossQShQm1d+dXMHw41FQdwOTpS2zepiBVVsKUKS5JZGHKjnQc1u4wnv3RsyzZuITznzmf6ppqrr7a3TEbPx4bdJcllihMaM1esJphJ9ewZ0drJj7/Neef0jfXITUsL7/sVgUK2W2n2ob1HMbDZz7M61+8zi+m/YJeveD00+HRR6G6ZRs3a6DVKDKqSa4DMCaeOYvWMHTYbqq3HcRfn/2KH59emuuQGp6JE10D9vDhuY6kTlcOvJJF6xfxu/d/R0mHEq699npGjYJ/vCKM7NHDEkWGWY3ChM68JWs57sRKqre25dEpq7j0TEsSgVu/Hl55xXWJzfKUHem675T7OLvv2dww7Qaa9n2dQw7xRmrbWIqMs0RhQmX+F+s49oRt7N7cgYf/vpKrzj4i1yE1TFOm5HTKjnQUFhQy6dxJ9O/UnwteOI9RF67jtddg+UHHWBtFhlmiMKGxYHmE8qFb2PVNZ/785HKuOSd300k0eBMnuuk6jjoq15H40qpZK1664CVaNGnBS61GUVCgPPrVGbBhgxuynXieF1MPlihMKCxcsZ5BQzdRteFg/jBhKdf9YECuQ2q4lixxa4vmUW0iVnGbYl4Y/QJrCz7ioAHvMH5+Gbu69oArrnAzRw4eDHfd5aYlsRHbwVDV0D+AEcBiYClwS13HDxo0SP3o3Dlep0u3P8gy2bxWvv6bWrXZlfxipv7uuENVRLWiIteR1Eub9jvif+6arlctKHAvOnZUveQS1cmTVTduVNVwf5+y+R2MB5ijcX6ninsvvESkEPgcOBWoAD4ELlDVzxKVKSsr0zlz5vi4RuL3Ev33pFMmm9dqiP8mEwBVOOww6N0bpk/PdTT1kuwzNHf2ItrMnU3rmW9y0PRXaLZhvWu0HzIEeedfCcvl+vuUze9g/PPIXFUtq70/H7rHlgNLVXUZgIhMBkYCCRNFkJq3T3SZxD1xEpdJt1yYywR/Lfr3T3ItUy/V1bBsGdx5Z64jyahBx/YD+gGXuh0F1VBQicytSlou99+n4L+DQciHRHEIELt6egVwbO2DRGQMMAagODpaMwAd2i+Lu/+rjYl/MInKpFsuzGUycS1KrTtsRp14olsJqAG78NaZVFYqOyuVqio3CL2qCnZVCZ+8/N2E5XL9fcrEdzAI+XDr6TzgdFW9ynt9MVCuqj9LVMZuPTXMf5MxscL+eQ37dzD+eeLfesqHXk8VQPeY192A1TmKxRhjGp18SBQfAn1EpJeINANGA1ODvECi2UeTzUqaTplsXqsh/puMiRX2z2vYv4N+hP7WE4CInAH8ASgEHlfVscmO93vryRhjTH73ekJVXwFeyXUcxhjTGOXDrSdjjDE5ZInCGGNMUpYojDHGJGWJwhhjTFJ50evJLxGJANmcoL4DsD6L1/PL4ktfmGMDi6++whxfLmLroaoda+9skIki20RkTrwuZWFh8aUvzLGBxVdfYY4vTLHZrSdjjDFJWaIwxhiTlCWKYIzLdQB1sPjSF+bYwOKrrzDHF5rYrI3CGGNMUlajMMYYk5QlCmOMMUk1ukQhIo+LyDoR+bTW/vNEZIGI1IhIwi5pItJORKaLyBJv2zbmvQEi8r53nk9EpHmc8r1EZLZX/ilv6vToe8NEZIOIVIvI9jDFJyJtRWSFF9sOETkiB7H9VESWioiKSIeY/T8WkfkislFEdovI0hz93yWKb5iIbPZ+trtFZG3I4msjIl96sVWKyOU5iG2SiCwWkU/FfUebevv7eWX3iMhWCfh763125sU8akTk6DjlE30vRET+JCJbvO9GoJ+9AOIb6X035onIHBEZmiiGpFS1UT2AE4GBwKe19pcAfYEZQFmS8r8FbvGe3wLc7z1vAswHjvJetwcK45SfAoz2nj8M/MR7fhBuHfAfevEtDFl8/xd43Ivtc+CNHMR2DNATWAF0iNl/HNDW+9n+FNiRo/+7RPENA14OwWcvUXy3AZOinztgI9Asy7GdAYj3+Dv7PnedgMHA34AHgv6/q3XMkcCyBOUTfS/OAF71fraXBv3ZCyC+Vuxrix4ALEoUQ7JH1n9Rh+HhfVk+TfBeXT/QxUBX73lXYHHMB+aJOq4ruJGWTbzXQ4DXvOfXAf8V4vj+AQyNxgZ8AXTOVmy1zrOCmF90td4bAOzO9v9dsvjwEkUuf7Z1xHcr8JAX22JgKVCQi9i8Mv8BjK217y5gbND/d7WOubf2dVP4XjwCXBDzs62KXicM8dU6bgi1/gBN9dHobj0FoLOqrgHwtp28/YcDKiKvichHInJznLLtgU2qWu29rgAOiSnfVkRmAC/hahhhiu/fwLne8xZAD9yytNmKLVXnA1vTLJvJ+IaIyL+BvwJFIYvvf3F/+X4AHAbcoKo1uYjNu+V0MTDN5/XTjS/W+bjaTG3JvheHAKtijt0d814Y4kNEzhGRRbg/9q5IIzZLFAFqgvuL+8fe9hwRGV7rmHhLoEf7JzcBBgFnApcAHUXk8BDFdx/u9s4ruA/mx0B1nOMzFVudROQk3JdpbV3H+lTf+D7CzaFzFC5RFIcsvtOBeUA5rqb4vyJyYI5iewiYqar/Cuj6KRGRY3G3jT6N93acfZrCe4GpR3yo6vOq2g8YBdyTzvUtUdRBRP7iNQRFV9hbKyJdvfe6Auu8/RXA26q6XlV34H6hDqx1uvXAQSISXVmwG7A6pvw0Vd0OfANsB44KS3yqukVVL8fdSqgAOgLLsxhbXf8PA4DHgKuBPSmWyUp83v/dNu/lDHf6fY3JuY4PuBx4znu+C/dz7Zft2ETkTtzn6pc+Yq9vfFGjif/XOtT9ve0ec2zTmPfCEN9eqjoTODSVz15tlijqoKqXq+rRqnqGt2sqrtEKb/ui9/w1YICIHOD9wL6La5yOPZcCb+EarGuXfxE4wSvbHDgA17AYivhE5CDZ10OrLe6vvi3Zii0ZESnG/aK7mDqSV47i6yIi0b/6osl/Q1jiA74Eon/lF+IaX5dlMzYRuQpXs7kgjdte9YkPESkAzgMmJzhXsu/tVOAS7+d7DLAnegspDPGJyGHRz56IDASakcJnL95FGtUDl5XX4O4lVgBXevvP8V5X4W5d7NcY5B3XHngDWOJt28W8dxGwANfY+9sE5Xvj7gUvBZ4GimLeuwnY7MW2J0zx4RrCtuJuN9UAX+Ugtp9716nG/cX0mLf/MVwt7Bvv/64mR/93ieL7qVf2G9xf7NUhi+9g9n0nFNfrKduxVeNue83zHnd4+7t414/+XDUDn71hwKw6fm8k+l4I8Gfcd2N3hn629YnvV97//TzgfWCon9+X0YdN4WGMMSYpu/VkjDEmKUsUxhhjkrJEYYwxJilLFMYYY5KyRGGMMSYpSxTGGGOSskRhjDEmqf8Pi/FEFKKPEgsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "birthday = df.loc['1990-11-06':'1990-11-06']\n",
    "plt.plot(birthday['DNI'], color='r') \n",
    "plt.plot(birthday['DHI'], color='g', marker='.') \n",
    "plt.plot(birthday['GHI'], color='b', marker='s') \n",
    "plt.ylabel('Irradiance [W/m$^2$]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise\n",
    "\n",
    "How does the Irradiance look like in YOUR birthday?\n",
    "\n",
    "Hint: the next cell is 'Markdown', you need to switch it to 'Code' for it to run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "birthday = df.loc[] # Type your birthday here using the 1990 year, i.e. '1990-11-06':'1990-11-06'\n",
    "plt.plot(birthday['DNI'], color='g') \n",
    "plt.plot(birthday['DHI'], color='b', marker='.') \n",
    "plt.plot(birthday['GHI'], color='r', marker='s') \n",
    "plt.ylabel('Irradiance [W/m$^2$]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GHI, DHI, and DNI are the three \"basic\" ways of measuring irradiance, although each of them is measured in units of power per area (watts per square meter):\n",
    "\n",
    "- GHI: Global Horizontal Irradiance; the total sunlight intensity falling on a horizontal plane\n",
    "- DHI: Diffuse Horizontal Irradiance; the subset of sunlight falling on a horizontal plane that isn't coming directly from the sun (e.g., the light that makes the sky blue)\n",
    "- DNI: Direct Normal Irradiance; the subset of sunlight coming directly from the sun\n",
    "\n",
    "![Overview](images/t1_GHI.PNG)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "![Overview](images/t1_DHI.PNG)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Overview](images/t1_DNI.PNG)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Later tutorials will show how these three values are used in PV modeling.  For now, let's just get a qualitative understanding of the differences between them: looking at the above plot, there is a pattern where when DNI is high, DHI is low.  The sun puts out a (roughly) constant amount of energy, which means photons either make it through the atmosphere without scattering and are counted as direct irradiance, or they tend to get scattered and become part of the diffuse irradiance, but not both.  Looking at DNI makes it easy to pick out which hours are cloud and which are sunny -- most days in January are rather overcast with low irradiance, but the sun does occasionally break through.\n",
    "\n",
    "In addition to daily variation, there is also seasonal variation in irradiance.  Let's compare a winter week with a summer week, using pandas to select out a summertime subset.  Notice the increased intensity in summer -- GHI peaks around 900 W/m^2, whereas in winter the maximum was more like 500 W/m^2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summer_week = df.loc['1990-06-01':'1990-06-08']\n",
    "summer_week[['GHI', 'DHI', 'DNI']].plot()\n",
    "plt.ylabel('Irradiance [W/m$^2$]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temperature\n",
    "\n",
    "Next up is temperature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_week['DryBulb'].plot()\n",
    "plt.ylabel('Ambient Temperature [°C]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wind speed\n",
    "\n",
    "And finally, wind speed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_week['Wspd'].plot()\n",
    "plt.ylabel('Wind Speed [m/s]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregating hourly data to monthly summaries\n",
    "\n",
    "Pandas makes it easy to roll-up timeseries data into summary values. We can use the [`DataFrame.resample()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.resample.html) function with [`DateOffsets`](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) like `'M'` for months.  For example, we can calculate total monthly GHI as a quick way to visualize the seasonality of solar resource:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990-01-31 00:00:00-05:00     74848\n",
       "1990-02-28 00:00:00-05:00     85751\n",
       "1990-03-31 00:00:00-05:00    131766\n",
       "1990-04-30 00:00:00-05:00    162302\n",
       "Freq: M, Name: GHI, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summing hourly irradiance (W/m^2) gives insolation (W h/m^2)\n",
    "monthly_ghi = df['GHI'].resample('M').sum()\n",
    "monthly_ghi.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ELEnvHUMx+eMl4PCImJ3aJwHnAqsCVwBHRERIGgOcT7Ek1DPAfhGxMG0zDTgudeXfIuK8Ef0pmJnZiA06UEXEPEkrAZ+JiI8PZ6eS3g3cDExZwf5BMbj8F8Vg0nA0cHVEnCrp6PT6S5K2BKYCWwF/DfxC0mYR8RJwFjA99euK1LcrKQa1JRGxiaSpFNfg9kuD4fHAZIojynmSZjUGRDMzq0fb1dPTL/ix6TYfwzGNYpml04CdgRci4pHGYzg7iogbgMUDmvcEGkc35wF7NbVfFBEvRMTDwAJge0nrA2tExE0RERSD3l4t9nUpsIskAbsBcyJicRqc5tCZgdfMzIahzKy/hcCvJM0C/tRobF6pYqCIOBhA0hbAB4FzJa0JXAtcBfwqDYIjtV5ENGYjPiHpzal9A4ojpoa+1PZiej6wvbHNY2lfyyQ9B6zT3N5im37SrUymA4wfP37kVZmZ2asMeT8q4HGKVSheB6ze9BhSRNwfEWdExBTg/cAvgX2BW0bW3SG1+iJytGkf6Tb9GyNmRMTkiJg8duzYUh01M7NyykxPPxFA0urFy/jjSIIi4i8U14auGMn2Azwpaf10NLU+8FRq7wM2bPrcOIqBti89H9jevE2fpJWBNSlONfYBOw3Y5roO9N3MzIahzB1+t5Z0B3APMF/SPElbldm5pDGS/lHSsZK+3HisaKeBWRTXwUg/L2tqn5pyNwI2BW5Npwmfl7RDuv50wIBtGvvaB7gmXceaDewqaS1JawG7pjYzM6tRmWtUM4AvRMS18Mqt6c8G3lNi28uA5ygmVrwwxGdbknQhxZHNupL6KGbinQpcIukg4FGK04lExHxJlwD3AsuAQ5uuhR3C8unpV6YHwDnABZIWUBxJTU37WizpZOC29LmTImLgpA4zM6tYmYFqtcYgBRAR10lareT+x6XrUyMWEfsP8tYug3z+FOCUFu1zabH0U0QsJQ10Ld6bCcws3VkzM+u4MpMpHpL0fyVNSI/jKFZUL+NGSV5CyczMRqzMEdWngBMpFpJt3OH3wHYbSLqbYobcysCBkh5ihCtTmJnZ6FZm1t8SitvQD8eHRtYdMzOz/totSttyMdqGdovSDnf1CTMzs8G0O6JqLEYrill+n66+O2ZmZv21W5T2+sZzSX9sfm1mZlaXMrP+oM0pwFYkHSnpnWmlBzMzsxFrd41q7aaXK6XVGV5Z/26IL7+OA/4T2ELSXcCNwK+Am/ylWTMzG452Rzzz6L846+1N7wWw8WAbRsQ/A6Tbg0ymWMXiU8DZkp6NiC1XpNNmZjZ6tLtGtVEH9r8qsAbFQq9rUiwEe3cH9mtmZqNEJdeQJM2guMvu8xS39LgR+LrvjluPCUdfPqzPLzx194p6Yma24spOphiu8cAY4PfA7yhumfFsRVlmZpaxSo6oImJKup3GVhTXp44Ctpa0mGJCxfFV5JqZWX7Kzvp7laFm76V7Ot0j6VmKW308R7G00vYUt+owMzMb0nBm/TVrO+tP0hHAu4H3Ai+SpqZT3DLDkynMzKy0qmb9vRW4FPh8uruumZnZiJS6RpW+7Lsp8IZGW0Tc0GaTnSLiC0Ps8/aI2K5UL83MbNQacqCS9GngCIrVJu4EdqA4jff+Nps1VqQYdLcU36syMzNrq8wR1RHAO4GbI2JnSVtQ3EixnbeX2O9LJT4zKEmbAxc3NW0MfBl4E/AZYFFqPzYirkjbHAMclLIPj4jZqX0ScC7FF5SvAI6IiJA0BjgfmAQ8A+wXEQtXpN9mZjY8ZQaqpRGxVBKSxkTE/WmQGFQd96OKiAeAiQCSVqL4vtaPKe4+fEZEfK3585K2BKZSTJn/a+AXkjaLiJeAs4DpwM0UA9UU4EqKQW1JRGwiaSpwGrBf1bWZmdlyZb7w2yfpTcBPgDmSLqNYCqmX7AL8dogBck/gooh4ISIeBhYA20taH1gjIm5KU+rPB/Zq2ua89PxSYJf0/TAzM6tJmVvR752eniDpWoprS1dV2qvhmwpc2PT6MEkHAHOBo9LSTRtQHDE19KW2F9Pzge2kn48BRMQySc8B6wBPN4dLmk5xRMb48eMH7eRwlzYCL29kZlZqCSVJ20k6HNgG6IuI/622W+WlFdr3AH6Qms4C3kZxWvAJ4PTGR1ts3u57Yu226d8QMSMiJkfE5LFjx5bvvJmZDanMrL8vA/sCP0pN/0/SDyLi3yrtWXkfBG6PiCcBGj8BJJ0N/Cy97AM2bNpuHMUpzL70fGB78zZ96SaQawK+n5aZjYjPqoxMmSOq/YF3RsTxaY2+HYCPVdutYdmfptN+6ZpTw97APen5LGCqpDGSNqL4Xtit6QvJz0vaIV1/OgC4rGmbaen5PsA16TqWmZnVpMysv4UUX/Rdml6PAX5bVYeGQ9JfAR8APtvU/FVJEylO0S1svBcR8yVdAtwLLAMOTTP+AA5h+fT0K9MD4BzgAkkLKI6kplZYjpmZtdBuUdpvUfyyfwGYL2lOev0B4Jf1dK+9iPgzxeSG5rZPtPn8KcApLdrnAlu3aF9KcdrTzMy6pN0R1dz0cx7F95MarqusN2ZmtsJyuxbWblHaxveHGjPrNksvH4iIF6vumJmZGZSb9bcTxZdeF1JM195Q0rQhFqU1MzPriDKTKU4Hdk1LFiFpM4pZdpOq7JiZmRmUm56+SmOQAoiI3wCrVNclMzOz5cocUc2VdA5wQXr9MYoJFmZmZpUrM1AdAhwKHE5xjeoG4MwqO2VmZtZQZlHaF4Cvp4eZmVmt2n3h925aLMDaEBHbVNIjMzOzJu2OqD5UWy/MzMwG0e4Lv5XfpdfMzGwog05Pl3SQpC82vf6dpD9Iel7SIfV0z8zMRrt236M6GJjZ9PqpiFgDGEtxaw0zM7PKtRuoXhcRzzS9/gG8sqL4qpX2yszMLGk3UK3Z/CIi/h1A0usYcGsNMzOzqrQbqH4uqdXt5k8Cfl5Rf8zMzPppNz39i8B3091tf53a3kFxn6pPV90xMzMzaD89/U/A/pI2BrZKzfdGRE/cht7MzEaHMksoPQQ8VENfzMzMXqXMbT56lqSFku6WdKekualtbUlzJD2Yfq7V9PljJC2Q9ICk3ZraJ6X9LJD0TUlK7WMkXZzab5E0ofYizcxGudf0QJXsHBETI2Jyen00cHVEbApcnV4jaUtgKsVpzCnAmZJWStucBUwHNk2PKan9IGBJRGwCnAGcVkM9ZmbWZMiBStLXJG011Od6yJ7Aeen5ecBeTe0XRcQLEfEwsADYXtL6wBoRcVNEBHD+gG0a+7oU2KVxtGVmZvUoc0R1PzAjnfo6WNKaQ25Rn6CYRj9P0vTUtl5EPAGQfr45tW8APNa0bV9q2yA9H9jeb5uIWAY8R4vvkEmaLmmupLmLFi3qSGFmZlYYcqCKiO9GxHuBA4AJwF2Svi9p56o7V8J7I2I74IPAoZJ2bPPZVkdC0aa93Tb9GyJmRMTkiJg8duzYofpsZmbDUOoaVbqWs0V6PE3xvaovSLqowr4NKSIeTz+fAn4MbA88mU7nkX4+lT7eB2zYtPk44PHUPq5Fe79tJK1MsVrH4ipqMTOz1spco/o68ADwD8C/R8SkiDgtIj4MbFt1B9v0azVJqzeeA7sC9wCzgGnpY9OAy9LzWcDUNJNvI4pJE7em04PPS9ohXX86YMA2jX3tA1yTrmOZmVlNhvweFcUv/+Mi4s8t3tu+w/0ZjvWAH6e5DSsD34+IqyTdBlwi6SDgUWBfgIiYL+kS4F5gGXBoRLyU9nUIcC7FYrtXpgfAOcAFaXWOxRSzBs3MrEbtbkW/XXp6J7DFwMluEXF7RDxXXdfaS19EfkeL9meAXQbZ5hTglBbtc4GtW7QvJQ10ZmbWHe2OqE5v814A7+9wX8zMzF6l3Vp/vTCrz8zMRrky16iQ9B6KqemvfD4izq+oT2ZmZq8YcqCSdAHwNoprVY3JB40VHMzMzCpV5ohqMrClp2WbmVk3lPnC7z3AW6ruiJmZWSvtpqf/lOIU3+rAvZJuBV5ovB8Re1TfPTMzG+3anfr7Wm29MDMzG0S76enXA0g6LSK+1PyepNOA6yvum5mZWalrVB9o0fbBTnfEzMyslXbXqA4B/gnYWNJdTW+tDtxYdcfMzMyg/TWq71MszvoV0u3ck+cjwre6MDOzWrS7RvUcxR1t90/3o1ovff6Nkt4YEY/W1EczMxvFyqxMcRhwAvAk8HJqDmCb6rplZmZWKLMyxZHA5un2GWZmZrUqM+vvMYpTgGZmZrUrc0T1EHCdpMvpvzLF1yvrlZmZWVJmoHo0PV6fHmZmZrUZcqCKiBMBJK1evIw/Vt6rEiRtSHGrkbdQTPKYERH/KekE4DPAovTRYyPiirTNMcBBFLcrOTwiZqf2ScC5wKrAFcARERGSxqSMScAzwH4RsbCWAs3MDChxjUrS1pLuoFhFfb6keZK2qr5rQ1oGHBURbwd2AA6VtGV674yImJgejUFqS2AqsBUwBTgzTbsHOAuYDmyaHlNS+0HAkojYBDgDOK2GuszMrEmZyRQzgC9ExFsj4q3AUcDZ1XZraBHxRETcnp4/D9wHbNBmkz2BiyLihYh4GFgAbC9pfWCNiLgp3XPrfGCvpm3OS88vBXaRpM5XY2ZmgykzUK0WEdc2XkTEdcBqlfVoBCRNALYFbklNh0m6S9JMSWultg0oZjA29KW2DdLzge39tomIZRSzH9dpkT9d0lxJcxctWjTwbTMzWwFlBqqHJP1fSRPS4zjg4ao7VpakNwI/BI6MiD9QnMZ7GzAReAI4vfHRFptHm/Z22/RviJgREZMjYvLYsWOHV4CZmbVVZqD6FDAW+BHw4/T8wCo7VZakVSgGqe9FxI8AIuLJiHgpIl6mOEW5ffp4H7Bh0+bjgMdT+7gW7f22kbQysCbgdQ7NzGo05EAVEUsi4vCI2C4ito2IIyJiSR2dayddKzoHuK/5O13pmlPD3hSTQABmAVMljZG0EcWkiVsj4gngeUk7pH0eAFzWtM209Hwf4Jp0HcvMzGrS7jYfs9pt2AO3on8v8Angbkl3prZjKRbRnUhxim4h8FmAiJgv6RLgXooZg4dGxEtpu0NYPj39yvSAYiC8QNICiiOpqZVWZGZmr9Lue1TvpphIcCHFJIWemu0WEb+kdZ+uaLPNKcApLdrnAlu3aF8K7LsC3TQzsxXUbqB6C8XdffcH/hG4HLgwIubX0TEzMzNoc40qTUi4KiKmUXyhdgHFmn+fq613ZmY26rVdQiktIbQ7xVHVBOCbFLP/zMzMatFuMsV5FNdtrgROjIh7BvusmZlZVdodUX0C+BOwGXB408pBolicdo2K+2ZmZjb4QBURZb4MbGZmVikPRmZm1tM8UJmZWU/zQGVmZj3NA5WZmfU0D1RmZtbTPFCZmVlP80BlZmY9zQOVmZn1NA9UZmbW0zxQmZlZT/NAZWZmPc0DlZmZ9TQPVGZm1tM8UJUgaYqkByQtkHR0t/tjZjaaeKAagqSVgG8DHwS2BPaXtGV3e2VmNnp4oBra9sCCiHgoIv4XuAjYs8t9MjMbNRQR3e5DT5O0DzAlIj6dXn8CeFdEHNb0menA9PRyc+CBYcasCzzdge4657WZ4ZzezXBOfRlvjYixrd5odyt6K6hFW7/RPSJmADNGHCDNjYjJI93eOa/tDOf0boZzeiPDp/6G1gds2PR6HPB4l/piZjbqeKAa2m3AppI2kvR6YCowq8t9MjMbNXzqbwgRsUzSYcBsYCVgZkTM73DMiE8bOieLDOf0boZzeiDDkynMzKyn+dSfmZn1NA9UZmbW0zxQmZlZT/NkippJEsVqFxtQfB/rceDW6PDFQuf0ZoZzejfDOT2c4ckU9ZG0K3Am8CDwu9Q8DtgE+KeI+LlzupOTUy255eRUS245ddVCRPhR0wO4D5jQon0j4D7ndC8np1pyy8mpltxy6qrF16jqtTLFShcD/Q5YxTldzcmpltxycqolt5xaavE1qnrNBG6TdBHwWGrbkGK1i3Oc09WcnGrJLSenWnLLqaUWX6OqWbqX1R4UFx5F8a+RWRFxr3O6m5NTLbnl5FRLbjm1ZHigMjOzXuZrVDWStKakUyXdL+mZ9Lgvtb3JOd3LyamW3HJyqiW3nLpq8UBVr0uAJcBOEbFORKwD7Aw8C/zAOV3NyamW3HJyqiW3nFpq8am/Gkl6ICI2H+57zqk+J6dacsvJqZbccuqqxUdU9XpE0r9IWq/RIGk9SV9i+YwZ53QnJ6dacsvJqZbccmqpxQNVvfYD1gGul7RE0mLgOmBt4KPO6WpOTrXklpNTLbnl1FKLT/2ZmVlP8xFVl0jart1r53QvJ6dacsvJqZbccqrM8EDVPYcM8do53cvJqZbccnKqJbecyjJ86s/MzHqa1/qrmZTPfWhyy8mpltxycqolt5xaMnxEVR9ldB+a3HJyqiW3nJxqyS2nrlo6cq8QP3rr3i3O6c0M5/RuhnN6NyPC96OqW073ocktJ6dacsvJqZbccnw/qgzldB+a3HJyqiW3nJxqyS3H96PKkTK6D01uOTnVkltOTrXkllNLhgcqMzPrZb5GVSNldB+a3HJyqiW3nJxqyS2nrlo8UNUrp/vQ5JaTUy255eRUS245vh9VbpTRfWhyy8mpltxycqolt5y6avERVb0eUT73ocktJ6dacsvJqZbccmqpxQNVvXK6D01uOTnVkltOTrXkluP7UZmZmfmIqkuU0X1ocsvJqZbccnKqJbecKjM8UHVPTvehyS0np1pyy8mpltxyfD8qMzMbnbzWX82kfO5Dk1tOTrXklpNTLbnl1JLhI6r6KKP70OSWk1MtueXkVEtuOXXV0pF7hfjRW/ducU5vZjindzOc07sZEb4fVd1yug9Nbjk51ZJbTk615Jbj+1FlKKf70OSWk1MtueXkVEtuOb4fVY6U0X1ocsvJqZbccnKqJbecWjI8UJmZWS/zNaoaKaP70OSWk1MtueXkVEtuOXXV4oGqXjndhya3nJxqyS0np1pyy/H9qHKjjO5Dk1tOTrXklpNTLbnl1FWLj6jq9YjyuQ9Nbjk51ZJbTk615JZTSy0eqOrVfO+WxarnPjTO6Z0M5/RuhnN6N8On/szMrLf5iMrMzHqaByozM+tpHqjMzKynea2/HiHpAxExp4P72xF4MiIekPS3wA4Uqxlf3qmMIfI7Xc+awBT63/NmdkQ826mMNtnZ1JLyO1pPHTm5/ZnlVk/VGT6i6h2dW8BR+gZwKnCBpJOBrwKrAp+X9B+dyhlCJ+s5ALgd2An4K2A1ii8VzkvvVS2nWqCzC59WnpPbn1lu9dSR4Vl/NZI0a7C3gPdHxGodypkPbE0xOP0O2CAi/ixpFeCOiNi6Qzl11fMA8K6B/9qUtBZwS0Rs1oGMbGpJ+6urnspzMvwzy6aeuv7MfOqvXu8DPg78cUB741bOnRIREZJebrxOP1+ms0fRddUjltfQ7OX0XifkVAvUV08dObn9meVUTy1/Zh6o6nUz8OeIuH7gG+lfWZ1yuaT/Ad4AfBe4RNLNwN8BN3Qwp656TgFul/Rzln/bfTzwAeDkDmXkVAvUV08dObn9meVUTy1/Zj71lylJ76Y4srpZ0tuAvYFHgUsj4uX2W/eedFpkN/rf82Z2RCzpasdGIKda6pLbn1lu9VTNA5WZmfU0z/qrkaQtJF0p6XJJb5N0rqRnJd0q6e0dzNlQ0kWS/kfSsWkSReO9n3Qwp656Ks/JqZbccnKqJbecumohIvyo6UFxfejDwP7AI8BUisP+DwNXdzBnDnAwMBH4FnAjsE56747XYD2V5+RUS245OdWSW05ttXRqR36U+ku9o+n5ggHv3d7BnDsHvP44MB94W4dz6qqn8pycasktJ6dacsupqxbP+qvXSk3Pvz7gvdd3MGcVSW+IiKUAEfHfkn4PzKb4cmGn1FVPHTk51ZJbTk615JZTSy2+RlWvb0t6I0BEnNlolLQJ8IsO5nwXeFdzQ0T8AtgXuKeDOXXVU0dOTrXklpNTLbnl1FKLZ/2ZmVlP8xFVl0m63Tm9mZNTLbnl5FRLbjlVZHig6r5OLpninNdehnN6N8M5PZLhgar7arnthnN6NsM5vZvhnB7J8DUqe02StF1E1HK6pEqS1gA2BR6KTJbPkbRuRDxd0b7XApZFxPNV7L8pZ22KJciy+Dt5rfMRVY+QdHcH95XbyhTbDXhMAmZJ2lbSdh3K+FTT83GSrpa0RNKNkjpy24W07/+WtG56vhvF99tOA+6UtG8HcxZL+q6kXSRVdrpH0gclPSzpl+nvYz5wi6Q+Sbt0KOOvJZ0v6TngaWC+pEclndD833YHcsan/28WAbcAt0l6KrVN6FTOEH14zf0eaJPfuVp8RFUfSR8Z7C3gOxExtkM5c4AfUqxsfBAwCfhwRDwj6Y6I2LZDOTcA/wG8keJGjV8CLgY+BBwZEZ36RfUyRS0vNDXvkNoiIt7fgYzbI2K79PwS4GrgbGBP4LAO1nJ3RPxNen4j8I8RsTANXldHxDs6lPMAxaok+wMTgEuBCyPi5k7svynnzpTxJuBnwO5RLIT8duB7jT/TFcy4BjgpIq5L/w+9DzgOOAZ4c0RMX9GMlHMT8A2KhZtfSm0rUXyt48iI2KFDOdn8HqitFg9U9ZH0IvA9Wt+LZp+IWL1DOXdGxMSm1x+n+J96D+AHnfjlkfb7yn/skhZExCZN793ewZx9gM8Bp0XEFant4YjYqBP7T/trHqgG/vl1cnCfD7w7Iv4g6ZfAjpFWs5c0PyK26lBOcz3jKZa2mUoxoFwUEcdWkPNYRGzY9F6/P8cVyPh18wAuaV5ETErP74+ILVY0I+3rwYjYdLjvjSAnm98DddXilSnqdRfwtYh41ZduJf19B3OyWpkiIi6VdBVwsqQDgaNo/T/Gihgn6ZsU/xIcK2mViHgxvdex00vAicC1kr4N/Ar4gaTLgPcDV3Uw55XTfRHxKPBV4KuSNqcYsDrlWUmfBdYAlkj6PHAJ8Pe8+mZ6I7Uo/ZK9Bvg/wEKAdEqzk5cv5kk6EziP5feJ2hCYBtzRwZycfg/UUouvUdXrSOAPg7y3dwdzcluZgoj4Y0R8Hvh3il8kHfmXWpMvAvOAucCxFKczkfQWYLDbbQ9bRFwC7AdsDmxGMaC/m+K03FGdygGuHST/gYg4sYM504DtKNaR3DW1zQY+CnymQxmfojgK+DnFf9eHpfa1KY4QOuUA4G6Kf0zMTnknUPw/84kO5hxJPr8HjqSGWnzqz15z0r+kV4+Iwf4HMbOMeKCqWZrptRfFnT0DeBy4LCI6edrHOT2aMYpyfhIRsyvO6HgtbfK/HBEnOaflvqr/f9MDVX0kfYPidM/5FLeeBhhHccrhwYg4wjndycmpltxy6qpliD48GhHjnfOq/XyDOv4780BVH0m/iYhXfScnncr6TQdnFTmnBzOc07sZaX+DnUoWsGpEdGTyWU45df3deDJFvZZK2r5F+zuBpc7pak5OteSWU1ctzwKbRsQaAx6rA084p6Va/m48Pb1enwTOkrQ6yw+TN6SYNfNJ53Q1p44M5/RuBhSnr94KPNnive87p6VPUsPfjU/9dUGa8rwBxSF4X0T83jm9kZNTLbnl1FWLDV/Vfzc+9dcFEfH7iJgXEXOBg53TOzk51ZJbTl21NJN0gnOGVvXfjQeq7tvDOT2bk1MtueXkVEtuOR3P8EDVfTndMC23nJxqyS0np1pyy+l4hq9RdZmk10ValNQ5vZWTUy255eRUS245VWR4oKrZKFmV4DWZk1MtueXkVEuOOYNkd271Cw9U9clptYDccnKqJbecnGrJMadNfsdW2fBAVaOcVgvILSenWnLLyamW3HLqWmXDkynqldNqAbnl5FRLbjk51ZJbzrPUsMqGV6ao1yfJZ7WA3HLqyHBO72Y4Z2RqWWXDp/66IKfVAnLLyamW3HJyqiXHnCr5iKo7xlH8y2YZxe26q/oPxzm9meGc3s1wzghImtyU8WBE3N/R/fuIqj6S/g44neK87iTgV8BawIvAJyLiMed0JyenWnLLyamW3HLqqoWI8KOmB3AHMDY93wj4cXr+AeDnzuleTk615JaTUy255dRVi2f91WuliFiUnj9KcRGSiJhDcQ7ZOd3LyamW3HJyqiW3nFpq8TWqes2VdA5wNbAncB2ApL8CVnJOV3NyqiW3nJxqyS2nllp8japGklYBPgNsCfwamBkRL0laFXhzRDzinO7k5FRLbjk51ZJbTm21eKAyM7Ne5mtUNZL0RkknSbpH0nOSFkm6WdInndPdnJxqyS0np1pyy6mtFh9R1UfSZcCPgV8AHwVWAy4CjgN+FxHHOqc7OTnVkltOTrXkllNXLR2ZOuhH6amcvx7w+rb083XA/c7pXk5OteSWk1MtueXUVYtP/dXrT5L+FkDSh4HFAFHcZKyTd8V0Tm9mOKd3M5zTuxk+oqrzAWwD3ErxLe5fApul9rHA4c7pXk5OteSWk1MtueXUVYuvUZmZWU/zqb8eIelA5/RmTk615JaTUy255XQyw0dUPUIdvG2zc157Gc7p3QzndD/DSyjVSNJdg70FrOec7uXkVEtuOTnVkltOXbV4oKrXesBuwJIB7QJudE5Xc3KqJbecnGrJLaeWWjxQ1etnwBsj4s6Bb0i6zjldzcmpltxycqolt5xaavE1KjMz62me9WdmZj3NA5WZmfU0D1RmZtbTPFCZmVlP80BlZmY97f8Dlvb9ACFsPsYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly_ghi = monthly_ghi.tz_localize(None)  # don't need timezone for monthly data\n",
    "monthly_ghi.plot.bar()\n",
    "plt.ylabel('Monthly Global Horizontal Irradiance\\n[W h/m$^2$]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also take monthly averages instead of monthly sums:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()  # add a second y-axis\n",
    "monthly_average_temp_wind = df[['DryBulb', 'Wspd']].resample('M').mean()\n",
    "monthly_average_temp_wind['DryBulb'].plot(ax=ax1, c='tab:blue')\n",
    "monthly_average_temp_wind['Wspd'].plot(ax=ax2, c='tab:orange')\n",
    "ax1.set_ylabel(r'Ambient Temperature [$\\degree$ C]')\n",
    "ax2.set_ylabel(r'Wind Speed [m/s]')\n",
    "ax1.legend(loc='lower left')\n",
    "ax2.legend(loc='lower right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Plot the Average DNI per Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You haven't finished this exercise correctly, try again!\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    daily_average_DNI = df[['']].resample('').mean()  # Add the column name, and resample by day. Month is 'M', day is..\n",
    "    daily_average_DNI.plot()\n",
    "except:\n",
    "    print(\"You haven't finished this exercise correctly, try again!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Where to get _Free_ Solar Irradiance Data?\n",
    "\n",
    "There are many different sources of solar irradiance data. For your projects, these are some of the most common:\n",
    "\n",
    "- [TMY3/TMY2](https://nsrdb.nrel.gov/data-sets/archives.html) - like the data we loaded earlier on this example. This database of TMY3 `.csv` files and TMY2 files have been archived by NREL.\n",
    "\n",
    "- [NSRDB](https://maps.nrel.gov/nsrdb-viewer/) - National Solar Radiation Database. You can access data through the website for many locations accross the world, or you can use their [web API](https://developer.nrel.gov/docs/solar/nsrdb/) to download data programmatically. An \"API\" is an [\"application programming interface\"](https://en.wikipedia.org/wiki/API), and a \"web API\" is a programming interface that allows you to write code to interact with web services like the NSRDB.\n",
    "\n",
    "- [EPW](https://www.energy.gov/eere/buildings/downloads/energyplus-0) - Energy Plus Weather data is available for many locations accross the world. It's in its own format file ('EPW') so you can't open it easily in a spreadsheet program like Excel, but you can use [`pvlib.iotools.read_epw()`](https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.iotools.read_epw.html) to get it into a dataframe and use it.\n",
    "\n",
    "- [PVGIS](https://ec.europa.eu/jrc/en/pvgis) - Free global weather data provided by the European Union and derived from many govermental agencies including the NSRDB. PVGIS also provides a web API. You can get PVGIS TMY data using [`pvlib.iotools.get_pvgis_tmy()`](https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.iotools.get_pvgis_tmy.html).\n",
    "\n",
    "- Perhaps another useful link: https://sam.nrel.gov/weather-data.html\n",
    "\n",
    "## Where else can you get historical irradiance data?\n",
    "\n",
    "There are several commercial providers of solar irradiance data. Data is available at different spatial and time resolutions. Each provider offers data under subscription that will provide access to irradiance (and other weather variables) via API to leverage in python.\n",
    "\n",
    "* [SolarAnywhere](https://www.solaranywhere.com/)\n",
    "* [SolarGIS](https://solargis.com/)\n",
    "* [Vaisala](https://www.vaisala.com/en)\n",
    "* [Meteonorm](https://meteonorm.com/en/)\n",
    "* [DNV Solar Resource Compass](https://src.dnvgl.com/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "\n",
    "![NSRDB Example](images/tutorial_1_NSRDB_example.PNG)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NREL API Key\n",
    "At the [NREL Developer Network](https://developer.nrel.gov/), there are [APIs](https://en.wikipedia.org/wiki/API) to a lot of valuable [solar resources](https://developer.nrel.gov/docs/solar/) like [weather data from the NSRDB](https://developer.nrel.gov/docs/solar/nsrdb/), [operational data from PVDAQ](https://developer.nrel.gov/docs/solar/pvdaq-v3/), or indicative calculations using [PVWatts](https://developer.nrel.gov/docs/solar/pvwatts/). In order to use these resources from NREL, you need to [register for a free API key](https://developer.nrel.gov/signup/). You can test out the APIs using the `DEMO_KEY` but it has limited bandwidth compared to the [usage limit for registered users](https://developer.nrel.gov/docs/rate-limits/). NREL has some [API usage instructions](https://developer.nrel.gov/docs/api-key/), but pvlib has a few builtin functions, like [`pvlib.iotools.get_psm3()`](https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.iotools.get_psm3.html), that wrap the NREL API, and call them for you to make it much easier to use. Skip ahead to the next section to learn more. But before you do...\n",
    "\n",
    "**Please pause now to visit https://developer.nrel.gov/signup/ and get an API key.**\n",
    "\n",
    "### Application Programming Interface (API)\n",
    "What exactly is an API? Nowadays, the phrase is used interchangeably with a \"web API\" but in general an API is just a recipe for how to interface with a application programmatically, _IE_: in code. An API could be as simple as a function signature or its published documentation, _EG_: the API for the `solarposition` function is you give it an ISO8601 formatted date with a timezone, the latitude, longitude, and elevation as numbers, and it returns the zenith and azimuth as numbers.\n",
    "\n",
    "A web API is the same, except the application is a web service, that you access at its URL using web methods. We won't go into too much more detail here, but the most common web method is `GET` which is pretty self explanatory. Look over the [NREL web usage instructions](https://developer.nrel.gov/docs/api-key/) for some examples, but interacting with a web API can be as easy as entering a URL into a browser. Try the URL below to _get_ the PVWatts energy output for a fixed tilt site in [Broomfield, CO](https://goo.gl/maps/awkEcNGzSur9Has18).\n",
    "\n",
    "https://developer.nrel.gov/api/pvwatts/v6.json?api_key=DEMO_KEY&lat=40&lon=-105&system_capacity=4&azimuth=180&tilt=40&array_type=1&module_type=1&losses=10\n",
    "\n",
    "In addition to just using your browser, you can also access web APIs programmatically. The most popular Python package to interact with web APIs is [requests](https://docs.python-requests.org/en/master/). There's also free open source command-line tools like [cURL](https://curl.se/) and [HTTPie](https://httpie.io/), and a popular nagware/freemium GUI application called [Postman](https://www.postman.com/).\n",
    "\n",
    "**If you have an NREL API key please enter it in the next cell.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "NREL_API_KEY = None  # <-- please set your NREL API key here\n",
    "\n",
    "# note you must use \"quotes\" around your key, for example:\n",
    "# NREL_API_KEY = 'DEMO_KEY'  # single or double both work fine\n",
    "\n",
    "# during the live tutorial, we've stored a dedicated key on our server\n",
    "if NREL_API_KEY is None:\n",
    "    try:\n",
    "        NREL_API_KEY = os.environ['NREL_API_KEY']  # get dedicated key for tutorial from servier\n",
    "    except KeyError:\n",
    "        NREL_API_KEY = 'DEMO_KEY'  # OK for this demo, but better to get your own key"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Fetching TMYs from the NSRDB\n",
    "\n",
    "The example TMY dataset used here is from an airport in North Carolina, but what if we wanted to model a PV system somewhere else? The NSRDB, one of many sources of weather data intended for PV modeling, is free and easy to access using pvlib. As an example, we'll fetch a TMY dataset for Albuquerque, New Mexico at coordinates [(35.0844, -106.6504)](https://www.google.com/maps/@35.0844,-106.6504,9z). We use [`pvlib.iotools.get_psm3()`](https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.iotools.get_psm3.html) which returns a Python dictionary of metadata and a Pandas dataframe of the timeseries weather data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Source': 'NSRDB',\n",
       " 'Location ID': '94139',\n",
       " 'City': '-',\n",
       " 'State': '-',\n",
       " 'Country': '-',\n",
       " 'Latitude': 35.09,\n",
       " 'Longitude': -106.66,\n",
       " 'Time Zone': -7,\n",
       " 'Elevation': 1496,\n",
       " 'Local Time Zone': -7,\n",
       " 'Dew Point Units': 'c',\n",
       " 'DHI Units': 'w/m2',\n",
       " 'DNI Units': 'w/m2',\n",
       " 'GHI Units': 'w/m2',\n",
       " 'Temperature Units': 'c',\n",
       " 'Pressure Units': 'mbar',\n",
       " 'Wind Direction Units': 'Degrees',\n",
       " 'Wind Speed': 'm/s',\n",
       " 'Surface Albedo Units': 'N/A',\n",
       " 'Version': '3.1.1'}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata, df_abq = pvlib.iotools.get_psm3(\n",
    "    latitude=35.0844, longitude=-106.6504,\n",
    "    api_key=NREL_API_KEY,\n",
    "    email='mark.mikofski@dnv.com',  # <-- any email works here fine\n",
    "    names='tmy')\n",
    "metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "TMY datasets from the PSM3 service of the NSRDB are timestamped using the real year that the measurements came from. The [`pvlib.iotools.read_tmy3()`](https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.iotools.read_tmy3.html) function had a `coerce_year` argument to force everything to align to a single dummy year, but `pvlib.iotools.get_psm3()` doesn't have that feature. For convenience let's standardize the data to 1990 and then compare monthly GHI to the North Carolina location:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_abq['Year'] = 1990\n",
    "df_abq.index = pd.to_datetime(df_abq[['Year', 'Month', 'Day', 'Hour']])\n",
    "\n",
    "ghi_comparison = pd.DataFrame({\n",
    "    'NC': monthly_ghi,  # using the monthly values from earlier\n",
    "    'NM': df_abq['GHI'].resample('M').sum(),\n",
    "})\n",
    "\n",
    "ghi_comparison.plot.bar()\n",
    "plt.ylabel('Monthly GHI [W h/m^2]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "It's not too surprising to see that our New Mexico location is significantly sunnier than the one in North Carolina."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Creative Commons License](https://i.creativecommons.org/l/by/4.0/88x31.png)](http://creativecommons.org/licenses/by/4.0/)\n",
    "\n",
    "This work is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}