Tutorial 1 - TMY Weather Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![tutorialpromo](images/tutorial_banner.PNG)\n",
    "\n",
    "\n",
    "# Tutorial 1 - Weather Data: Accesing it, understanding it, visualizing it!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "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."
   ]
  },
  {
   "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": {},
   "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": {
    "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/app/uploads/sites/243/2022/11/anemometer.jpg\"></img>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "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://www.sandia.gov/app/uploads/sites/243/2022/11/AmbTemp-768x1024-1.jpg\" width=\"400\" height=\"400\"> </img>\n"
   ]
  },
  {
   "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",
    "- [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/)"
   ]
  },
  {
   "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/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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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"
    }
   },
   "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"
    }
   },
   "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"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11.2\n"
     ]
    }
   ],
   "source": [
    "print(pvlib.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 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 Albuquerque, New Mexico Rico at coordinates [(35.06995837964615, -106.63433040356928)](https://maps.app.goo.gl/3GnTC9i8WGxDbXeh7). 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": 4,
   "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": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Source': 'NSRDB',\n",
       " 'Location ID': '1493238',\n",
       " 'City': '-',\n",
       " 'State': '-',\n",
       " 'Country': '-',\n",
       " 'Time Zone': -4,\n",
       " 'Local Time Zone': -4,\n",
       " 'Clearsky DHI Units': 'w/m2',\n",
       " 'Clearsky DNI Units': 'w/m2',\n",
       " 'Clearsky GHI Units': 'w/m2',\n",
       " 'Dew Point Units': 'c',\n",
       " 'DHI Units': 'w/m2',\n",
       " 'DNI Units': 'w/m2',\n",
       " 'GHI Units': 'w/m2',\n",
       " 'Solar Zenith Angle Units': 'Degree',\n",
       " 'Temperature Units': 'c',\n",
       " 'Pressure Units': 'mbar',\n",
       " 'Relative Humidity Units': '%',\n",
       " 'Precipitable Water Units': 'cm',\n",
       " 'Wind Direction Units': 'Degrees',\n",
       " 'Wind Speed Units': 'm/s',\n",
       " 'Cloud Type -15': 'N/A',\n",
       " 'Cloud Type 0': 'Clear',\n",
       " 'Cloud Type 1': 'Probably Clear',\n",
       " 'Cloud Type 2': 'Fog',\n",
       " 'Cloud Type 3': 'Water',\n",
       " 'Cloud Type 4': 'Super-Cooled Water',\n",
       " 'Cloud Type 5': 'Mixed',\n",
       " 'Cloud Type 6': 'Opaque Ice',\n",
       " 'Cloud Type 7': 'Cirrus',\n",
       " 'Cloud Type 8': 'Overlapping',\n",
       " 'Cloud Type 9': 'Overshooting',\n",
       " 'Cloud Type 10': 'Unknown',\n",
       " 'Cloud Type 11': 'Dust',\n",
       " 'Cloud Type 12': 'Smoke',\n",
       " 'Fill Flag 0': 'N/A',\n",
       " 'Fill Flag 1': 'Missing Image',\n",
       " 'Fill Flag 2': 'Low Irradiance',\n",
       " 'Fill Flag 3': 'Exceeds Clearsky',\n",
       " 'Fill Flag 4': 'Missing CLoud Properties',\n",
       " 'Fill Flag 5': 'Rayleigh Violation',\n",
       " 'Surface Albedo Units': 'N/A',\n",
       " 'Version': 'v3.2.2',\n",
       " 'latitude': 18.45,\n",
       " 'longitude': -66.1,\n",
       " 'altitude': 3}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmy, metadata = pvlib.iotools.get_psm3(\n",
    "    latitude=18.4671, longitude=-66.1185,\n",
    "    api_key=NREL_API_KEY,\n",
    "    email='silvana.ovaitt@nrel.com',  # <-- any email works here fine\n",
    "    names='2021')\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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function get_psm3 in module pvlib.iotools.psm3:\n",
      "\n",
      "get_psm3(latitude, longitude, api_key, email, names='tmy', interval=60, attributes=('air_temperature', 'dew_point', 'dhi', 'dni', 'ghi', 'surface_albedo', 'surface_pressure', 'wind_direction', 'wind_speed'), leap_day=True, full_name='pvlib python', affiliation='pvlib python', map_variables=True, url=None, timeout=30)\n",
      "    Retrieve NSRDB PSM3 timeseries weather data from the PSM3 API. The NSRDB\n",
      "    is described in [1]_ and the PSM3 API is described in [2]_, [3]_, and [4]_.\n",
      "\n",
      "    .. versionchanged:: 0.9.0\n",
      "       The function now returns a tuple where the first element is a dataframe\n",
      "       and the second element is a dictionary containing metadata. Previous\n",
      "       versions of this function had the return values switched.\n",
      "\n",
      "    .. versionchanged:: 0.10.0\n",
      "       The default endpoint for hourly single-year datasets is now v3.2.2.\n",
      "       The previous datasets can still be accessed (for now) by setting\n",
      "       the ``url`` parameter to the original API endpoint\n",
      "       (``\"https://developer.nrel.gov/api/nsrdb/v2/solar/psm3-download.csv\"``).\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    latitude : float or int\n",
      "        in decimal degrees, between -90 and 90, north is positive\n",
      "    longitude : float or int\n",
      "        in decimal degrees, between -180 and 180, east is positive\n",
      "    api_key : str\n",
      "        NREL Developer Network API key\n",
      "    email : str\n",
      "        NREL API uses this to automatically communicate messages back\n",
      "        to the user only if necessary\n",
      "    names : str, default 'tmy'\n",
      "        PSM3 API parameter specifing year (e.g. ``2020``) or TMY variant\n",
      "        to download (e.g. ``'tmy'`` or ``'tgy-2019'``).  The allowed values\n",
      "        update periodically, so consult the NSRDB references below for the\n",
      "        current set of options.\n",
      "    interval : int, {60, 5, 15, 30}\n",
      "        interval size in minutes, must be 5, 15, 30 or 60. Must be 60 for\n",
      "        typical year requests (i.e., tmy/tgy/tdy).\n",
      "    attributes : list of str, optional\n",
      "        meteorological fields to fetch. If not specified, defaults to\n",
      "        ``pvlib.iotools.psm3.ATTRIBUTES``. See references [2]_, [3]_, and [4]_\n",
      "        for lists of available fields. Alternatively, pvlib names may also be\n",
      "        used (e.g. 'ghi' rather than 'GHI'); see :const:`REQUEST_VARIABLE_MAP`.\n",
      "        To retrieve all available fields, set ``attributes=[]``.\n",
      "    leap_day : bool, default : True\n",
      "        include leap day in the results. Only used for single-year requests\n",
      "        (i.e., it is ignored for tmy/tgy/tdy requests).\n",
      "    full_name : str, default 'pvlib python'\n",
      "        optional\n",
      "    affiliation : str, default 'pvlib python'\n",
      "        optional\n",
      "    map_variables : bool, default True\n",
      "        When true, renames columns of the Dataframe to pvlib variable names\n",
      "        where applicable. See variable :const:`VARIABLE_MAP`.\n",
      "    url : str, optional\n",
      "        API endpoint URL.  If not specified, the endpoint is determined from\n",
      "        the ``names`` and ``interval`` parameters.\n",
      "    timeout : int, default 30\n",
      "        time in seconds to wait for server response before timeout\n",
      "\n",
      "    Returns\n",
      "    -------\n",
      "    data : pandas.DataFrame\n",
      "        timeseries data from NREL PSM3\n",
      "    metadata : dict\n",
      "        metadata from NREL PSM3 about the record, see\n",
      "        :func:`pvlib.iotools.parse_psm3` for fields\n",
      "\n",
      "    Raises\n",
      "    ------\n",
      "    requests.HTTPError\n",
      "        if the request response status is not ok, then the ``'errors'`` field\n",
      "        from the JSON response or any error message in the content will be\n",
      "        raised as an exception, for example if the `api_key` was rejected or if\n",
      "        the coordinates were not found in the NSRDB\n",
      "\n",
      "    Notes\n",
      "    -----\n",
      "    The required NREL developer key, `api_key`, is available for free by\n",
      "    registering at the `NREL Developer Network <https://developer.nrel.gov/>`_.\n",
      "\n",
      "    .. warning:: The \"DEMO_KEY\" `api_key` is severely rate limited and may\n",
      "        result in rejected requests.\n",
      "\n",
      "    .. warning:: PSM3 is limited to data found in the NSRDB, please consult the\n",
      "        references below for locations with available data. Additionally,\n",
      "        querying data with < 30-minute resolution uses a different API endpoint\n",
      "        with fewer available fields (see [4]_).\n",
      "\n",
      "    See Also\n",
      "    --------\n",
      "    pvlib.iotools.read_psm3, pvlib.iotools.parse_psm3\n",
      "\n",
      "    References\n",
      "    ----------\n",
      "\n",
      "    .. [1] `NREL National Solar Radiation Database (NSRDB)\n",
      "       <https://nsrdb.nrel.gov/>`_\n",
      "    .. [2] `Physical Solar Model (PSM) v3.2.2\n",
      "       <https://developer.nrel.gov/docs/solar/nsrdb/psm3-2-2-download/>`_\n",
      "    .. [3] `Physical Solar Model (PSM) v3 TMY\n",
      "       <https://developer.nrel.gov/docs/solar/nsrdb/psm3-tmy-download/>`_\n",
      "    .. [4] `Physical Solar Model (PSM) v3 - Five Minute Temporal Resolution\n",
      "       <https://developer.nrel.gov/docs/solar/nsrdb/psm3-5min-download/>`_\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pvlib.iotools.get_psm3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Explore your weatherfile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's display the first 4 lines of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</th>\n",
       "      <th>temp_air</th>\n",
       "      <th>temp_dew</th>\n",
       "      <th>dhi</th>\n",
       "      <th>dni</th>\n",
       "      <th>ghi</th>\n",
       "      <th>albedo</th>\n",
       "      <th>pressure</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-01 00:30:00-04:00</th>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>25.6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 01:30:00-04:00</th>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>25.5</td>\n",
       "      <td>19.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 02:30:00-04:00</th>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>25.4</td>\n",
       "      <td>19.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1029.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 03:30:00-04:00</th>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>25.4</td>\n",
       "      <td>19.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1029.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Year  Month  Day  Hour  Minute  temp_air  temp_dew  \\\n",
       "2021-01-01 00:30:00-04:00  2021      1    1     0      30      25.6      20.0   \n",
       "2021-01-01 01:30:00-04:00  2021      1    1     1      30      25.5      19.8   \n",
       "2021-01-01 02:30:00-04:00  2021      1    1     2      30      25.4      19.7   \n",
       "2021-01-01 03:30:00-04:00  2021      1    1     3      30      25.4      19.8   \n",
       "\n",
       "                           dhi  dni  ghi  albedo  pressure  wind_direction  \\\n",
       "2021-01-01 00:30:00-04:00  0.0  0.0  0.0    0.08    1030.0            75.0   \n",
       "2021-01-01 01:30:00-04:00  0.0  0.0  0.0    0.08    1030.0            76.0   \n",
       "2021-01-01 02:30:00-04:00  0.0  0.0  0.0    0.08    1029.0            77.0   \n",
       "2021-01-01 03:30:00-04:00  0.0  0.0  0.0    0.08    1029.0            77.0   \n",
       "\n",
       "                           wind_speed  \n",
       "2021-01-01 00:30:00-04:00         6.1  \n",
       "2021-01-01 01:30:00-04:00         6.0  \n",
       "2021-01-01 02:30:00-04:00         5.8  \n",
       "2021-01-01 03:30:00-04:00         5.7  "
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 8760\n",
      "Number of columns: 14\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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tmy.iloc[0];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tmy.loc['2021-01-01 01:30:00-04:00'];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also print all the column names in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Year', 'Month', 'Day', 'Hour', 'Minute', 'temp_air', 'temp_dew', 'dhi',\n",
       "       'dni', 'ghi', 'albedo', 'pressure', 'wind_direction', 'wind_speed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "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": [
    "### 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": 13,
   "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>temp_air</th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-01 00:30:00-04:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 01:30:00-04:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 02:30:00-04:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.4</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 03:30:00-04:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.4</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 04:30:00-04:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 05:30:00-04:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.4</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 06:30:00-04:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.7</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 07:30:00-04:00</th>\n",
       "      <td>70.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 08:30:00-04:00</th>\n",
       "      <td>262.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>26.9</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 09:30:00-04:00</th>\n",
       "      <td>437.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>634.0</td>\n",
       "      <td>27.2</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 10:30:00-04:00</th>\n",
       "      <td>577.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>27.4</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 11:30:00-04:00</th>\n",
       "      <td>755.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>890.0</td>\n",
       "      <td>27.6</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 12:30:00-04:00</th>\n",
       "      <td>792.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>904.0</td>\n",
       "      <td>27.6</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 13:30:00-04:00</th>\n",
       "      <td>638.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>27.6</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 14:30:00-04:00</th>\n",
       "      <td>641.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>848.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             ghi    dhi    dni  temp_air  wind_speed\n",
       "2021-01-01 00:30:00-04:00    0.0    0.0    0.0      25.6         6.1\n",
       "2021-01-01 01:30:00-04:00    0.0    0.0    0.0      25.5         6.0\n",
       "2021-01-01 02:30:00-04:00    0.0    0.0    0.0      25.4         5.8\n",
       "2021-01-01 03:30:00-04:00    0.0    0.0    0.0      25.4         5.7\n",
       "2021-01-01 04:30:00-04:00    0.0    0.0    0.0      25.3         5.5\n",
       "2021-01-01 05:30:00-04:00    0.0    0.0    0.0      25.4         5.4\n",
       "2021-01-01 06:30:00-04:00    0.0    0.0    0.0      25.7         5.5\n",
       "2021-01-01 07:30:00-04:00   70.0   46.0  215.0      26.3         5.8\n",
       "2021-01-01 08:30:00-04:00  262.0   85.0  553.0      26.9         6.4\n",
       "2021-01-01 09:30:00-04:00  437.0  120.0  634.0      27.2         6.7\n",
       "2021-01-01 10:30:00-04:00  577.0  162.0  651.0      27.4         6.8\n",
       "2021-01-01 11:30:00-04:00  755.0  112.0  890.0      27.6         6.7\n",
       "2021-01-01 12:30:00-04:00  792.0  114.0  904.0      27.6         6.5\n",
       "2021-01-01 13:30:00-04:00  638.0  208.0  598.0      27.6         6.3\n",
       "2021-01-01 14:30:00-04:00  641.0  107.0  848.0      27.5         6.3"
      ]
     },
     "execution_count": 13,
     "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_tmy = df_tmy[['ghi', 'dhi', 'dni', 'temp_air', 'wind_speed']]\n",
    "# show the first 15 rows:\n",
    "df_tmy.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": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_week = df_tmy.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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "birthday = df_tmy.loc['2021-11-06':'2021-11-06']\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 this 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": [
    "classweek = 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(classweek['DNI'], color='g') \n",
    "plt.plot(classweek['DHI'], color='b', marker='.') \n",
    "plt.plot(classweek['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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_week['temp_air'].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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_week['wind_speed'].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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sayala\\AppData\\Local\\Temp\\1\\ipykernel_23136\\3896696835.py:2: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_ghi = df_tmy['ghi'].resample('M').sum()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2021-01-31 00:00:00-04:00    153522.0\n",
       "2021-02-28 00:00:00-04:00    159212.0\n",
       "2021-03-31 00:00:00-04:00    204065.0\n",
       "2021-04-30 00:00:00-04:00    195086.0\n",
       "Freq: ME, Name: ghi, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summing hourly irradiance (W/m^2) gives insolation (W h/m^2)\n",
    "monthly_ghi = df_tmy['ghi'].resample('M').sum()\n",
    "monthly_ghi.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "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 Insolation\\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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "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_tmy[['temp_air', 'wind_speed']].resample('ME').mean()\n",
    "monthly_average_temp_wind['temp_air'].plot(ax=ax1, c='tab:blue')\n",
    "monthly_average_temp_wind['wind_speed'].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": 25,
   "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_tmy[['']].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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}