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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   "source": [
    "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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    "## 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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    "### 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.  "
   ]
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    "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"
   ]
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   "source": [
    "\n",
    "![Overview](images/t1_DHI.PNG)\n",
    "\n"
   ]
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   "source": [
    "![Overview](images/t1_DNI.PNG)\n"
   ]
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    "### 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/app/uploads/sites/243/2022/11/anemometer.jpg\"></img>"
   ]
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    "### 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://www.sandia.gov/app/uploads/sites/243/2022/11/AmbTemp-768x1024-1.jpg\" width=\"400\" height=\"400\"> </img>\n"
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    "## 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",
    "- [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/reference/generated/pvlib.iotools.read_epw.html) to get it into a dataframe and use it.\n",
    "\n",
    "- [PVGIS](https://re.jrc.ec.europa.eu/pvg_tools/en/) - 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/reference/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.dnv.com/)"
   ]
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   "source": [
    "\n",
    "![NSRDB Example](images/tutorial_1_NSRDB_example.PNG)\n"
   ]
  },
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   "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/reference/generated/pvlib.iotools.get_psm3.html), that wrap the NREL API, and call them for you to make it much easier to use.\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"
   ]
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   "source": [
    "## Now to Coding:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. First Step for Google Collab:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If running on google colab, uncomment (remove the # sign) from the next cell and execute it to install the dependencies and prevent \"ModuleNotFoundError\" in later cells.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install -r https://raw.githubusercontent.com/PV-Tutorials/2024_PVSC/main/requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   },
   "source": [
    "### 1. 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   "outputs": [],
   "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": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Query which version you are using of pvlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9.4.dev19+ge4356f9\n"
     ]
    }
   ],
   "source": [
    "print(pvlib.__version__)"
   ]
  },
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     "slide_type": "slide"
    }
   },
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    "### 2. Fetching TMYs from the NSRDB\n",
    "\n",
    "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 San Juan, Puerto Rico at coordinates [(18.4671, -66.1185)](https://goo.gl/maps/ZuYuKFxSpJ1z9HXX8). We use [`pvlib.iotools.get_psm3()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_psm3.html) which returns a Python dictionary of metadata and a Pandas dataframe of the timeseries weather data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Please pause now to visit https://developer.nrel.gov/signup/ and get an API key.**\n",
    "\n",
    "\n",
    "**If you have an NREL API key please enter it in the next cell.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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"
   ]
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   "cell_type": "code",
   "execution_count": 22,
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    "slideshow": {
     "slide_type": "subslide"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sayala\\Documents\\GitHub\\pvlib-python\\pvlib\\iotools\\psm3.py:349: pvlibDeprecationWarning: PSM3 variable names will be renamed to pvlib conventions by default starting in pvlib 0.11.0. Specify map_variables=True to enable that behavior now, or specify map_variables=False to hide this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Source': 'NSRDB',\n",
       " 'Location ID': '1493571',\n",
       " 'City': '-',\n",
       " 'State': '-',\n",
       " 'Country': '-',\n",
       " 'Latitude': 18.45,\n",
       " 'Longitude': -66.1,\n",
       " 'Time Zone': -4,\n",
       " 'Elevation': 6,\n",
       " 'Local Time Zone': -4,\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 Units': 'm/s',\n",
       " 'Surface Albedo Units': 'N/A',\n",
       " 'Version': '3.2.0'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df, metadata = pvlib.iotools.get_psm3(\n",
    "    latitude=18.4671, longitude=-66.1185,\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": {},
   "source": [
    "What if we want a specific year, can NSRDB API provide it?\n",
    "Identify the answer with the cell below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pvlib' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m help(pvlib\u001b[38;5;241m.\u001b[39miotools\u001b[38;5;241m.\u001b[39mget_psm3)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'pvlib' is not defined"
     ]
    }
   ],
   "source": [
    "help(pvlib.iotools.get_psm3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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 into 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",
      "    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",
      "    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",
      "    field                       description\n",
      "    =====================       ======================================================================================================================================================\n",
      "    Index                       A pandas datetime index. NOTE, the index is timezone aware, and times are set to local standard time (daylight savings is not included)\n",
      "    ETR                         Extraterrestrial horizontal radiation recv'd during 60 minutes prior to timestamp, Wh/m^2\n",
      "    ETRN                        Extraterrestrial normal radiation recv'd during 60 minutes prior to timestamp, Wh/m^2\n",
      "    GHI                         Direct and diffuse horizontal radiation recv'd during 60 minutes prior to timestamp, Wh/m^2\n",
      "    GHISource                   See [1]_, Table 1-4\n",
      "    GHIUncertainty              Uncertainty based on random and bias error estimates see [2]_\n",
      "    DNI                         Amount of direct normal radiation (modeled) recv'd during 60 mintues prior to timestamp, Wh/m^2\n",
      "    DNISource                   See [1]_, Table 1-4\n",
      "    DNIUncertainty              Uncertainty based on random and bias error estimates see [2]_\n",
      "    DHI                         Amount of diffuse horizontal radiation recv'd during 60 minutes prior to timestamp, Wh/m^2\n",
      "    DHISource                   See [1]_, Table 1-4\n",
      "    DHIUncertainty              Uncertainty based on random and bias error estimates see [2]_\n",
      "    GHillum                     Avg. total horizontal illuminance recv'd during the 60 minutes prior to timestamp, lx\n",
      "    GHillumSource               See [1]_, Table 1-4\n",
      "    GHillumUncertainty          Uncertainty based on random and bias error estimates see [2]_\n",
      "    DNillum                     Avg. direct normal illuminance recv'd during the 60 minutes prior to timestamp, lx\n",
      "    DNillumSource               See [1]_, Table 1-4\n",
      "    DNillumUncertainty          Uncertainty based on random and bias error estimates see [2]_\n",
      "    DHillum                     Avg. horizontal diffuse illuminance recv'd during the 60 minutes prior to timestamp, lx\n",
      "    DHillumSource               See [1]_, Table 1-4\n",
      "    DHillumUncertainty          Uncertainty based on random and bias error estimates see [2]_\n",
      "    Zenithlum                   Avg. luminance at the sky's zenith during the 60 minutes prior to timestamp, cd/m^2\n",
      "    ZenithlumSource             See [1]_, Table 1-4\n",
      "    ZenithlumUncertainty        Uncertainty based on random and bias error estimates see [1]_ section 2.10\n",
      "    TotCld                      Amount of sky dome covered by clouds or obscuring phenonema at time stamp, tenths of sky\n",
      "    TotCldSource                See [1]_, Table 1-5\n",
      "    TotCldUncertainty           See [1]_, Table 1-6\n",
      "    OpqCld                      Amount of sky dome covered by clouds or obscuring phenonema that prevent observing the sky at time stamp, tenths of sky\n",
      "    OpqCldSource                See [1]_, Table 1-5\n",
      "    OpqCldUncertainty           See [1]_, Table 1-6\n",
      "    DryBulb                     Dry bulb temperature at the time indicated, deg C\n",
      "    DryBulbSource               See [1]_, Table 1-5\n",
      "    DryBulbUncertainty          See [1]_, Table 1-6\n",
      "    DewPoint                    Dew-point temperature at the time indicated, deg C\n",
      "    DewPointSource              See [1]_, Table 1-5\n",
      "    DewPointUncertainty         See [1]_, Table 1-6\n",
      "    RHum                        Relatitudeive humidity at the time indicated, percent\n",
      "    RHumSource                  See [1]_, Table 1-5\n",
      "    RHumUncertainty             See [1]_, Table 1-6\n",
      "    Pressure                    Station pressure at the time indicated, 1 mbar\n",
      "    PressureSource              See [1]_, Table 1-5\n",
      "    PressureUncertainty         See [1]_, Table 1-6\n",
      "    Wdir                        Wind direction at time indicated, degrees from north (360 = north; 0 = undefined,calm)\n",
      "    WdirSource                  See [1]_, Table 1-5\n",
      "    WdirUncertainty             See [1]_, Table 1-6\n",
      "    Wspd                        Wind speed at the time indicated, meter/second\n",
      "    WspdSource                  See [1]_, Table 1-5\n",
      "    WspdUncertainty             See [1]_, Table 1-6\n",
      "    Hvis                        Distance to discernable remote objects at time indicated (7777=unlimited), meter\n",
      "    HvisSource                  See [1]_, Table 1-5\n",
      "    HvisUncertainty             See [1]_, Table 1-6\n",
      "    CeilHgt                     Height of cloud base above local terrain (7777=unlimited), meter\n",
      "    CeilHgtSource               See [1]_, Table 1-5\n",
      "    CeilHgtUncertainty          See [1]_, Table 1-6\n",
      "    Pwat                        Total precipitable water contained in a column of unit cross section from earth to top of atmosphere, cm\n",
      "    PwatSource                  See [1]_, Table 1-5\n",
      "    PwatUncertainty             See [1]_, Table 1-6\n",
      "    AOD                         The broadband aerosol optical depth per unit of air mass due to extinction by aerosol component of atmosphere, unitless\n",
      "    AODSource                   See [1]_, Table 1-5\n",
      "    AODUncertainty              See [1]_, Table 1-6\n",
      "    Alb                         The ratio of reflected solar irradiance to global horizontal irradiance, unitless\n",
      "    AlbSource                   See [1]_, Table 1-5\n",
      "    AlbUncertainty              See [1]_, Table 1-6\n",
      "    Lprecipdepth                The amount of liquid precipitation observed at indicated time for the period indicated in the liquid precipitation quantity field, millimeter\n",
      "    Lprecipquantity             The period of accumulatitudeion for the liquid precipitation depth field, hour\n",
      "    LprecipSource               See [1]_, Table 1-5\n",
      "    LprecipUncertainty          See [1]_, Table 1-6\n",
      "    PresWth                     Present weather code, see [2]_.\n",
      "    PresWthSource               Present weather code source, see [2]_.\n",
      "    PresWthUncertainty          Present weather code uncertainty, see [2]_.\n",
      "    =====================       ======================================================================================================================================================\n",
      "    \n",
      "    .. admonition:: Midnight representation\n",
      "    \n",
      "       The function is able to handle midnight represented as 24:00 (NREL TMY3\n",
      "       format, see [1]_) and as 00:00 (SolarAnywhere TMY3 format, see [3]_).\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",
      "    .. [1] Wilcox, S and Marion, W. \"Users Manual for TMY3 Data Sets\".\n",
      "       NREL/TP-581-43156, Revised May 2008.\n",
      "    .. [2] Wilcox, S. (2007). National Solar Radiation Database 1991 2005\n",
      "       Update: Users Manual. 472 pp.; NREL Report No. TP-581-41364.\n",
      "    .. [3] `SolarAnywhere file formats\n",
      "       <https://www.solaranywhere.com/support/historical-data/file-formats/>`_\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pvlib.iotools.read_tmy3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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": 6,
   "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": 6,
     "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": 7,
   "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tmy.iloc[0];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "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": 10,
     "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": {},
   "source": [
    "### 4. Explore your weatherfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head(24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 5. 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": 11,
   "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": 11,
     "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": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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 typically look like in the confernece week? \n",
    "\n",
    "Hint: the next cell is 'Markdown', you need to switch it to 'Code' for it to run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "conferenceweek = df.loc[] # Type the conference week here, using the last code cell as example i.e. '1990-11-06':'1990-11-06'\n",
    "plt.plot(conferenceweek['DNI'], color='g') \n",
    "plt.plot(conferenceweek['DHI'], color='b', marker='.') \n",
    "plt.plot(conferenceweek['GHI'], color='r', marker='s') \n",
    "plt.ylabel('Irradiance [W/m$^2$]');"
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Temperature\n",
    "\n",
    "Next up is temperature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_week['Wspd'].plot()\n",
    "plt.ylabel('Wind Speed [m/s]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 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": 17,
   "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": 17,
     "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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": 20,
   "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": {},
   "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 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}