PVPMC_Problem Based Learning - Solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8439c511",
   "metadata": {},
   "source": [
    "![tutorial banner](images/PVLib_PVPMCLogo.PNG)\n",
    "\n",
    "\n",
    "# PVPMC Problem Based Learning\n",
    "\n",
    "\n",
    "### Goal: Model energy loss due to high wind stowing.\n",
    "\n",
    "\n",
    "This is just one quick example of solution, there are other ways to achieve this!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "41eba374",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9.0\n"
     ]
    }
   ],
   "source": [
    "import pvlib\n",
    "import pandas as pd  # for data wrangling\n",
    "import matplotlib.pyplot as plt  # for visualization\n",
    "import pathlib  # for finding the example dataset\n",
    "import os\n",
    "\n",
    "print(pvlib.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d72ab8bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# during the live tutorial, we've stored a dedicated key on our server\n",
    "NREL_API_KEY=None\n",
    "\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": 15,
   "id": "136a3554",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Source': 'NSRDB',\n",
       " 'Location ID': '615057',\n",
       " 'City': '-',\n",
       " 'State': '-',\n",
       " 'Country': '-',\n",
       " 'Latitude': 37.77,\n",
       " 'Longitude': -100.02,\n",
       " 'Time Zone': -6,\n",
       " 'Elevation': 794,\n",
       " 'Local Time Zone': -6,\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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dodge_Kansas, metadata = pvlib.iotools.get_psm3(\n",
    "    latitude=37.7528, longitude=-100.017,\n",
    "    api_key=NREL_API_KEY,\n",
    "    email='mark.mikofski@dnv.com',  # <-- any email works here fine\n",
    "    names='tmy')\n",
    "\n",
    "metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9e52cefb",
   "metadata": {},
   "outputs": [],
   "source": [
    "location = pvlib.location.Location(latitude=metadata['Latitude'],\n",
    "                                   longitude=metadata['Longitude'])\n",
    "\n",
    "times = df_tmy.index - pd.Timedelta('30min')\n",
    "solar_position = location.get_solarposition(times)\n",
    "solar_position.index += pd.Timedelta('30min')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6fd8830c",
   "metadata": {},
   "outputs": [],
   "source": [
    "tracking_angles = pvlib.tracking.singleaxis(\n",
    "    apparent_zenith=solar_position['apparent_zenith'],\n",
    "    apparent_azimuth=solar_position['azimuth'],\n",
    "    axis_tilt=0,\n",
    "    axis_azimuth=0,\n",
    "    max_angle=50,\n",
    "    backtrack=True,  # for true-tracking\n",
    "    gcr=0.5)  # irrelevant for true-tracking\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "db462553",
   "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>tracker_theta</th>\n",
       "      <th>aoi</th>\n",
       "      <th>surface_azimuth</th>\n",
       "      <th>surface_tilt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01 01:00:00-05:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 02:00:00-05:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 03:00:00-05:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 04:00:00-05:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 05:00:00-05:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-31 20:00:00-05:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-31 21:00:00-05:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-31 22:00:00-05:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-31 23:00:00-05:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-01-01 00:00:00-05:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           tracker_theta  aoi  surface_azimuth  surface_tilt\n",
       "1990-01-01 01:00:00-05:00            NaN  NaN              NaN           NaN\n",
       "1990-01-01 02:00:00-05:00            NaN  NaN              NaN           NaN\n",
       "1990-01-01 03:00:00-05:00            NaN  NaN              NaN           NaN\n",
       "1990-01-01 04:00:00-05:00            NaN  NaN              NaN           NaN\n",
       "1990-01-01 05:00:00-05:00            NaN  NaN              NaN           NaN\n",
       "...                                  ...  ...              ...           ...\n",
       "1990-12-31 20:00:00-05:00            NaN  NaN              NaN           NaN\n",
       "1990-12-31 21:00:00-05:00            NaN  NaN              NaN           NaN\n",
       "1990-12-31 22:00:00-05:00            NaN  NaN              NaN           NaN\n",
       "1990-12-31 23:00:00-05:00            NaN  NaN              NaN           NaN\n",
       "1991-01-01 00:00:00-05:00            NaN  NaN              NaN           NaN\n",
       "\n",
       "[8760 rows x 4 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracking_angles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7ebc6f61",
   "metadata": {},
   "outputs": [],
   "source": [
    "stowing_angle=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "78a89b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ec9b422dc0>]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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9rtXH76SZrOa1KtastNsovR1olZfKYoa6epHyv2Z9Jt4j6+qF51/AJdpQbTLDvJW49nZIXBelbwaMEIUF6KBMI6ldS71YMXp9WzVWjGy73bwEanLUGBjPThO6L08bgtk7g5Fr3jTT/GUKkgWXMh/Js+2OErnr6WNz1K6l0lhyYirGaP3Gjw8OoVPsTiMgs2YVelFmhrcmjZEOYiZdWCaRrmdBY8WpB3BQrF4rhPGtw4wfe5CMLK9o9Iq9XqhYBJKO08RrxW6gJ1oNW9oYO7kRODl4DD+7h/pUTLpUqX7sylKbvaVhfMCpET7FbiIIlhHqDczFZFhQ7OqvlfaDdwHAmqJKw2sJ+yWhCcxctf3VtdhQsh+VVeqePWYprqgytQbiFUb70/Y95r1ntu85aKifBYl9Bw+jwsH7rsbG0v26a0rVNfVp188NF035mN17wNw18Hp9SIvwKXYNrNzfr5cX6R53sNo5dyWtTmjmYbW2uBJDJPlLExTsKE9rz+qiCny9LOZFJO18W3TcQvuPnoXLX/oBfUc6FyDs/JEz8MCH9jxQnMSMVTdrbQkuGj1LN8aPlA0l+3HR6Fl48wfvPSOMInXJTSwin/3sNPR6Ju1DckfZVLofl/3zB+RJEqwrcdc7izBm1gbNMqZQGWZyr5ZznpuG856frlxYgWdVvOf8eA+IlGK3wlKJz6ka+y0odtW0XKbPpM4ihaiQW3crW5TLC/elbUtE1VMj4b9/wKa3gJxZa4MTWsKMtbdqZwUAc4nLE/7xCzft1inpH9I3Mi+tTqOB57bsPoj5G1PdkqVPZKtjysjUS7WJ67G5TNlQ8uNdLXSKXSs5hZUnoxBGXAH1z2x0jjZIGYy8kCQsc9dW5TT6OXxYroOnITVM1CVXwk5MxUTOo0lC6BS7FlZuk/wY61+xGTtSq1Rin53BRaR8fICeJ6HHSkC1QKt1B6xfK5hRrPLnohk/dr/hqRgfqBfK3cvuCrmdDOleWRKBVjYhQ/WeBVvn+IotAwbeTMU4AU/FGCD5pNb5aMEoRrwVjKXhSi2kNtC1vkhN7HLDAAnCa2fQLSsr6MY5iV/3IM/EpE5reHePzNSkbbE7Ik6kCJ9ij/9fVF6F7BG5KXFcrNzfiqpajPhsRfL3kvgK/SeSDCxGxuSOvanBrib8uMWCNOZ5fdYGvPT92uRvgv51sPPp9BuzNyB7RC7G68TBUUI+AN+cvRF//26NJTnGztmIUd+kH3v/B+573KzYUY5f/3tBShiCxyR9SMqu8lj8mtlrS7HPZJCy7XsOYvDrP6YdV1JRhcFj5qGkQn3xcdaaEmSPyMVsWYrH2rp63P72Qvy0RTkdo3xhe1t8Mf4/P23D45+np5urqqnDzW/NR4HCgnLu8iI8OOlnVRnNILWun/1qFYx4IAdF4fNUjAneW7AFQCznoRyzF/Kz/MLk318ujSWBMrMaDgBvz00N2PXpkkLFctLO9sjA7splTNT79+/W4rWZ5lzB8rdpu5dpMfrb2ENEzbVLC3m7/vbtGrw+y5ob4Mipa/CWggvhtyt3WTqfERqiDJZh8eY9WFNUqXvMmJkNAdi+KTAn2xuzN2LZ9n3IlblXfrhoG5YVluOjxdtUj/2fCbFAePe+l5eyfce+Q5i/cTf+9MkyQzIk3DQf+2wFJi1OTze3dPs+/LRlL579Kr0/DP8oH18tcyapmtRCH/9j6pjXexsN8tuSW4RWsWvh9IPaSMcw+g2TtBO2O7K5YhkrAcR06/XYelFewA2ICSXBrkRWkkQ4XYcWat1S2g/tKD47OjMtyY3VqxW8buU7oVPsiU7ohvKzg3F3R5cFQTAVKBDs8eeEUad03YN2K5LuwgblctPaTRPBjPujCU8eeVsDdktcIXSK3QhO90UnV8+NdCp3Fk/9J2hKzmmC2D6tRcfkNgf6tyVr26Iij/2WnEblPI1xCiZBJBW7L+5FRqdiJL1QPhgCqBdsYyc3pZdYeeAa+QbCTpuVcmhqyWMG56JaemP0yGtJdXc015jGoO9DodgXbtqN+z9Ygvp6kbyh+w6qB+eRpkirqxeYtaYE89aXmQ4+tSX+ifC+Q/reDEY7+CENGRqUfvAUYFVNnakYKXJ2lVehYEeFI7LoefX8sM56yILSyupkarOYzMbDByhNxRRXNGR10gt2tXjzHnyeX5j8xD+Rqk2IWB/Wm2L7cUMZyvZXp4QvqJElpHljdmyhXZqycN6G1Ou1cqd6m42mOnzxm/QsWLV19Zi1tgRLtu7B3gOH08bj6qIK1Ncrt1WeHUtpuMmPkd67WWtLsGjTbny2pDA5ug4YTFlXVB5r87cFu1L6VkllFSYt3pbcr4Yfo9lWBiUiOgbAOABnICb/PUIIe/mlFLjvvTzsr67FxMXbsEwh5okcaTCfcXM34cW4W9zN53Y0Ve/F/5iNLaMGYeEmZdcwKUatgCc+L8C4ITkA7H3EpIaa7tALTazHU1MKMFnF00eLRLUXjJphKKmJEZ78osG1cMrPO9L2Dxm/GJPv74ec7LaGz5m4FFe+Mge7DxzGllGD0G/UDAgBbBk1SPOYBHrNe3vuJtzZt7Pq/kRqtv7ds/D+PX2wOB4LaMKPW7Cp7ABev703BvXqoHjshpJK3DFukeK+2WtLcHGP9thQUpnm2bIs7tUiZdBr85J/y9t44aiZitdD2r++XLoD//4hPa3j67M24uXp6wAAp3U4CqWVqe6az+euBhHhua9X4fXbe6fsW12UahR0atMyre4Zq1NdOx+Ip2Us3HsI/yNJlznm9nMAAMsLjT20+70Ya/P98eB1ifb/8uU5SQNTrY8AMdfTx6481VBdTmFLsQN4FcC3QoibiKgZgJZ6B1ghEYRr575DphMWb5ckKd5Y6kyyWiWMKsn1JfoucnaoM/VUMK7Z19tM9Otk1Np1kmuoFkbXSgJyANgtOc6sx4te+W0GQ/7KlViiD++K+60r3bXyQ+rWZ2nc2lUqs8dCAnApSv2+cK+yBStNMi9vY4LE/dyl4aMPAG1aNk3+nbjsxZXGgoqp5SY1i3zWoFlmhmLi+LL95nPx2sWyYieiowD0B3A3AAghDgOw10v06rR0TMNRbgZiynDQK8aOmGbifru5uOSqN4WkiV4ukOl6Xzj00i3vI2ToszNDZ3bgHO6SXFcwYaCY9QJzy2ssSGtIdubYuwIoBfAuEf1MROOIqJW8EBENJaI8IsorLbUfrtXOCr6bOsCogpHefDcWxWpNKHYvEkNZHURGj7P7sHZyjDuXos28f7dbDzjj/dqh+jxY2gyO+nUPO4q9CYDeAN4UQpwD4ACAEfJCQoixQogcIUROVlaWjeqsIe2YrlqoBjtkvYG3QDsdz5TF7uIgilKaMa9RttiD+X2C467FSYvd+DGJotznGrCj2AsBFAohEis2kxFT9K5BpG29KHV8r261kw8NO+O3TojQxP4OAkG5b1LSPr5z1SBxBunY8/xLVrPX3aXno/r99348WlbsQohdALYTUY/4pssAmA8gYhKjT+VEJhipkpOv/jvJ3PVlyB6RixU6K+079h1C9ojcmCulSk+wM1f3g4nsRMdIFqCkvDZjPbo+nmu67p8lMWiufm2uZtlhHzTEMLl1rHFHqoc//jnltx0l0nfkDDz1RQEAoLKqFtkjGtpsJDjZocN1KTGBjN63i0bPxGOT0wNqJdgrW5RTc5OUbjVyGdww+G96K3bv8rftw0EN98HCvQfxuYIHk5xx8dhPL0xNd5eUcrvEA8jseGnetEHtPfLJUlPHqjFx0VZVKdTGmZvY9WN/EMBEIloO4GwAI21LZANpx/UiEa8SX68wFvRo7oYy1X12BuAsWTQ/La46Q9l17qVp6yx5sSi5Hqrx3cri5N9G3EkTfLl0Z+riqYpKM/LWsquiCovjUQ7lXhhKwcnk96VE5oVh9L5t33MI/8lLD6ilRkKxW88UZB0z0xtSn305i0zcY7MkrovRh3znY2NLgdnHtsTn+cb7rBYvT1unuu8mk27WTmDL3VEIsRRAjjOi6EMgwzFiwpYVXopdy4onYrzBzRgkZufTjTzI3J6hS8is9DDIcPFTSLPjJSHdUS28saSbeOGlICMUX55K0eqc0huc8Of2fLrZwqJP8nd8g50AZ1Y+jW/YHr6HoZ/LCWkhIRy8flqnstpmt2+v1umNugOHlaANndApdqMkXl/DtFLuhB+smdamKybb1afXEbAO7yRuts3RuDM25AyDPrbaPKfvX5AMo1ApdiLtLxhTfMRNzrsFCdtTMTptVnMpC0631MbsoqFz9WpfIT+nYoKA1phz02I3+4brtU7w420lFIo9cV3+NXND8vNoKUu27sWcdaX479KGhct5G8qwaNNu2wPfbDozo2wtO5C2cDN1xS5MXlJo23LfvT9dZmkqNLVgaFrKJBEYS8qEHzdjb/wT/Nq6ery3YGtambXFlXh9ln6Gp5FTV+OLnxs8TKatKkbBjvK0eCJA6ufoamOmYEc5rnp1LgaPmYdP8rZDCIFxczehsqoGu8pjwZvMUFVTh7fnpMY/kX86PzmvQf76eoFHP03PUrRIEqBLi0k/KS+uCiGSWcMmLmy43lr9/On/rkTu8iJ8vTw1iJuRh4fSdSquqMLaXZX4/Uf5KdtXFVVg5NTV+FHmGCCESMlS5jTfxTNTGR3r1475UbfMTo1gZ6WV1fhgYXpfVzM61bKpuYndWDGekEGkGQPlxjfnp217/PNYoKgh/dSDLhnh0U/V3dLsME4hpd/S7fuwdPs+TBl+oa1zj1FQpNI4JUkvC1kZs4+TZ75ahXkbdmPckBxNL4+/f7dWdV+CsTKlOdRm7tJXZzSkpFs2eTnatGyG53NXY11xJZYXlmPNLnMxe96YvREHZFElX5J5QkjTBeauKFIMmnbL2IWK55cv9v9lSkFamT0HajBnfRkqq2JuhbsPHMaWsgPIbpf2wXcKh2rqMFymhIGG/L5a1NSJNCV3z4SfsHJneqyXh1Tym05fXYLZJtxwzfL5zzvw0i1n41iVjGRqaBlQt72dep9qJDFghn+UnwzQ1nAudVYXVaCyqgatj/DO7TEcFruNY+2+0LplsWth5zXcyJGZGcpzMVaqLY+HNLaTINsLEuGSyw/VoEzhjUaP/VXGQrzK6zPK2Lnp0RDlCCGS32ckqDXyKbMK1bX1hrxp5H70WiGzldDyb3eSFk0zHTuX0sxAAisB5rx20guFYg/TinoYQjWpXU4rU0B+L04bTvFmsnza8SabafaqLN22z4AMlBbfx4tkG2GZ7jfbf7XapemVZKqWxAmtHGSdUCh2O7ojPI+EBrwaSGrulmHCqMh2bQO3+5ER+YjSjRwh2WcWq2s5bvqk28Fs/9Uqr7UgG4ZhEtBblIqdQRXOuCnedB0nFXlYrrPy6oLzuHU9Mhz82MXq/Q/qG7RZ7xit0na+JVGuy9vHQSgUe1A7klu4PR+XjBbohB87pfwXWBoiJFo83uWpGEMWO9JDLSddDC3cAaOXQt5Pgnqvzd5arbUsvTSGQScUir2R6XV7lrSZL1/T/NitV+zXPbJyrcI45QTErnGmk8mjLV4Is4aWZ9fbwXqc1ute97lwKHYbx06Yv8VW3Wbvh9xtzwpPTVmhX8gO8Qsq77xWOt/izXvw5Bcr8EKudjQ+t/jbt/pRGO3y2OTleHtuunuqXbo+novBY2L5RY04t3yRv0NxikcIgWtfn6dwhDZGb/eAv89O+b2p7IByQRX+/Jk7LsNyzBomZlxe1VIwJrCaitEtQqHYwzYVI4TARae0s3z8umL3crNKSZuKsXieiYu2mcrc5C/m30vMRGI0Q70AlhWWQwiBqlp998id5VXpUzEQOFxXH+i3ELmLplu4eQ0+XGjugzY5Xt+eUCh2Pyf1rFQtBNBF56MRtzCURq2hcOqxQdYOIcOMLSKE8X7m6KJsxG63m93X6cVUtwmFYvfTXrdyOwX8m8c1U6+8s4ar61rDq/tiSrHD+FupUjGr3xIIiMAuhFrBTeUrNXqsGEBeG02hUOxOunh5Qb0QvmUsN1Nr2uKpN2/MvpAMfOavGIrUC2H4QSAv5sUHSmHBzebYnWkM3VQMEWUS0c9E9LUTAinW4daJXarbzwFjxjJIjxUTsZGughfWkxkrWgh/vgOInGIP8FSM19faiSBgDwNYDeAoB86liJ+Lp2uLzQWLAmKdYMnWfc4LY6hu/TL/+Wk79h48nIwSmGB5YTk6tW3pkmTBIIjrCKuKKjBtVbF+QQBTlqZGBJ23vgzFFekRMI0QvCthnYId5dhY6ozTgZIHzMRFDYunm016BfmBLcVORB0BDALwAoBHHJFIgWEDumLkVPfd2pSoNBn8KYE0tGzQ+KZgF76JhzqV8pvxi32QxhsK98YGq0DwFNp1r+uHkU0g987QS/qsRRAfcla55l/m3T3VuGj0LM39VqZlwvbl6SsA/gzA1dlZtaTLQSVC4yUyHDrs7QJCyDx0I8VDl53itwgAgNE39Wr4EZYPlIjoGgAlQgjNwNlENJSI8ogor7TUWkzmsA2SxjJXHSbC1oe8IKq9NCi+FtIp5DAtnl4I4Foi2gLgYwCXEtGH8kJCiLFCiBwhRE5WVpalisISYCoBW+zBhe9NA1G9Fk6GXbCD9AETmpACQojHhRAdhRDZAG4FMFMIcadjkkkIxm0yTkTHS2SIqkIzTzQvRFDco1Mt9nDNsXtCQB7AhonSolTU8OrOhOEtM+Zm6bcUzhOUNknlCGUGJSHEbCHENU6cSwm/s/SY5a53outdElYS+UnnrCtF+SFzqd2iSlTND7Op+1KPdSeYV96WPfqFHIQtdhdYun2f3yIwPhOGLhvVF0s7fuZO+qjX1DVc4O9WprsXu0k4FLvfAjCMScJgjLD3VjpOXhHplKyNnOOWCIViZ83OMIxR7KgLJ9fHpGfyOjpkKBR72ObYGSYMRHUqxg6OXhOh+KcnhEOxs15nQkYYjJGo6nU7HijO6nV7oX7tEA7F7rcADGOS4R/l+y2CLq9MX4etu7VTvoURO9MeN7+1wDE5pGJMX13i2HmNEA7FziY7wzjOptIDeGpKgd9iOE7Wkc39FgEA0LZVs5Tf9R46s4dDsfstQIi5+4Jsv0VgGE/JzAyGxmjaJFW9ejkZEw7FHoz7xDBMCAjqorCX8+zhUOxsszMMEzZketzLsAKhUOys1xmGCRvyD8C8/CAsFIqdp2IYhjFOMOdiKg5Zy8ZmhXAodr8FYBgmNPTqeIzfIgAA2rc+IuX3NwVFntUdDsXeSE32Zwf3xGkdXMsRzjCh4p83n2Wo3G19TnJZkgb6d1dPHtTzhNSxW13jXcCYUCj2gMTN95yrzuiA7scdaescHBueiQp9ux2Lo1s01SzTLO5i2KJpphci4cRjjlDdJzdIvbRPQ6HYG6tXTAbZX0lntc5EhQwCMnWsvERaPK+CbpmpxsuZh3Ao9sap10FEtr9WY4OdiQoZRCnp5pTLxP73qtubUuzuiZGGZcVORJ2IaBYRrSailUT0sJOCMbGOYNfy4JjbTFQg6E/LJvKdejUFaWZ8eTml3MTGsbUA/iSEyCei1gCWENE0IcQqh2RL0ngtduhaKHocrK5zSBqG8Z8mOtqxsirmUujVm2qr5sZVqN40kpNYttiFEEVCiPz435UAVgM40SnBpDSWOfarzzw+5ffRLZrimWt7Gj7+hevPSNv2rccpuYzQpqX2ApgWwwZ0dVASRsrFPdQ9PNxkzO3nGCrX7sjmOKdzm+TvO/uqe7+0PiJV4b55R2+ccLT6QqdVhvXvZrjszTmdHK9fDUfm2IkoG8A5ABYp7BtKRHlElFdaWmrx/MbLHmniCSrng3v7WD7WCd6441xsGTUo+ZuIkNW6Oe77RZeUcq3jbfzD5aekbL/+HPPP1fsHdMO5ksFihjfv6G3puG5Z1j19Hr/qtOTfd/XtbPk8TDpd2rXypd4rex6vWyYzg5CRQSkW+/PXnalaXm6wX3VmBzx6RQ+rIiryq7NOwPEmHhZHeOSpAzig2InoSACfAfiDEKJCvl8IMVYIkSOEyMnKsmYRmLHXo+gamSFvVGKBSNZ7laZt9F5JBYTnLpE86x9MgvxmnOijRiWsU3A6aExTurYUOxE1RUypTxRCfO6MSIr1GC6bpgRNEFQPErnCVmuh1fl4q8226r7ViMZXqPDLKDLSjxJ62mif82IsB/kbETteMQTgHQCrhRAvOSeSQl0mytpdbAwiagNO3q2sLs5Y9ai0eqmdukURvNW+EobraVREL/zYg6vW7VnsFwK4C8ClRLQ0/u9qh+RKwUyHi+RUTNoXbMqNtNR2Yd3yiOClbtT4FbrDTK1GZVSainGcAGt2yyuNQoh58Ghsm+twNqZiLB/pLkanl5SukxE/W6vWjfWpGH4kBJFQWOwGZfRkKiawGiMkX56aoVPbFrplmjUJV7Nr6lKDBx1/VHwlXqX39ut6bPLv447SXrUXAM444WhLcrVqbm2V/7QOrS0dx7hLkKcxE7aNUQnPOemYtG1OGxQBnmIPr2L/67U98ecr092XhvXvpjnX/NClJ2PVX6/AAxcb9z/1gl4dG5TrrEcvxsw/DUj+lvt9XyXxd//6wV+k7PvovvPx6q1nJ3/3yW6b/PuTYf3S6hVCYOT16m5jCYZf0i3NvbLD0foPUSWe/lVPnHGi+aiVw/rHfNgv6BZ7cMnv8sjrz8S43+QYOtcN55yI6Y/0x03ndjQth1kGn32C63U4gV9qnQg49Xjth33CmDH67Png3vNxr8xNWI3Ox7Y0dtIQEVrFPuSCbBwrywIOxBYQm2tY5L+75GQ0yczAY1ee6qZ4urSTZVLv1Lahc3Vp1wpdJb7ecv/XhOUhAJxxYqq1fcHJ7dBexUrv06Wt4nYjUz2DzjwB3Y+zb2mfeEwLZGQQBp1pXtn1jLd14OnHKe6//fyTcLnKPjm/Pq8TTm7fGudlW/PhN0P341qH4i3RT4vd6PcnRmVs1iQDT1x9mn5BNBgKZmGLndHF3AKSc/Ua7ZwZGemLs36/uNtZ7PNa9swAT3Mk8EtEItKdrU7ca3dktOomHFzNHjnFTvBf4VhBS0nZaY9e1zPaNWOWknPxpYMyKLxayA2Dt5afIhr3zArBhQwAoVbsaoOysWRc8vJVkBAOr4mgEoo+6aOMhtW6CyJaPSdPxXiM1n0Kw/jSw8kmGO2cpBAL22u3xYRV58SA8lLRCiFC0e/8fKsw6nZuRka3mxNgvR49xR6GAQSky6kptkqjjExnGIkVY4QMcnbg++3L7nU/8TJkq1WC7O6YwO9+ExYip9gBhHIazsyY8mP8ZRCl1Wvns207c+xBtpSUECIsStM/jM6xuzIVY/E4nooJEGF84rspsWGvGKK06YsA9+vAEQKD3VYAPbvo9cNE1/PLe0yZ4I6AyCl2olAa7JZkNqKUnfI+UcrmFOTodobxoLMIhMNiDwO2XFwb0S2InGIHQuKBIEPT3VE+H2+mfQ7p3oyM9HcdL+IsaRGm2xwGxe6niEYNEPaKMUZEFbvfEpjHYmBG2xie24SScrIxT25DeCfeFLzsIkLw4qke9fXa+xumYoJzHQOs10Ou2BXucZBuvFPYaZNTHyiRgleM3xZLFO+1n/j57HGjK/nx5h4UozLcil2FgFxbcwRcaEL64oWdwRiUAeCFGAICGSEYaX4+KP31irEYUkBB5qCEjghBdzOJznUNyqfscrQ6l1pfccJiNv6BktLiqfv1RgWeY3eGIM1oKXXhoNzn6Cn2kOKWH7ueJWR40QoKit3nh6SdMeTl+BMiOJacFkF2OkgYPm7I6OgpA3IJ7SazvpKI1hLRBiIa4ZRQdglyB7WCWmu8VKyk8IGSX1Z3GK39MHRJP0X0Ikep0yiJHJQHuJ1k1pkAXgdwFYDTAdxGRKc7JZhVgnFZzWPqwwsfWqm0eOr3YHTiKnhhBITFj91Xd0ejU4LuimEKJZGD4v1kx2LvA2CDEGKTEOIwgI8BDHZGLGM0zUy/iE0yMtCymbWUbU0M3BSn7ptcRq1EDE0zM2S/Y0I0NbAip5fgQX5uNQixa5u6zfzFaN40do5mBuuVkhg0TRLtt5G8IqFolfqQ0zTNILQ0mEjCT/x8+LTQGbMt4slmtJLoqJEYA/L+m9xvoS/Gjku/XkYThriNHcV+IoDtkt+F8W0pENFQIsojorzS0lLLlb3067Mw6bd98dzgnvjq97F0cNf0OgE3x1Ob3fuLLjiyeROc37UtPrz3fPzvFT1wdqdjAMRSqg08/Tj87xU90LxJZso5E7x669no1/VYDDz9OPQ+6Rg8Nei05PEAkPtQrM5FT1yOKcMvxLODewKIpaYbGk/ZJufoFk3xxh290adLW9xx/kkY1r8r3v5NDrpltcJHv+2L684+Abf16YRhA7risavUMzpde/YJGDagK967pw9evOFM3Nm3M4b174rfXdItKfvE+85POebdu8/Dm3f0xpNXn46czm0w6oZY+rs37+gNAHjllrMxrH9XPDKwe8p2IJamrFWzzGSKvctPa4+2rZol0/f93zWn4+HLTsFpHVrjlpxOAIAP7u2DG3vH7kXXdq1wSY8sPHn1aXj37vMw/ZH+aNE0E3f2PQkT7u4DALirX2cM7d8VF/fIAgDcfUE2Rt/UC62PaILTO8TS5nVs0wK/vagLAKB/9yxc2TOWEvD2+LX8/SUnY8rwC3FWp2Pw2QMNaf+++N0FAICXbzkLL95wJvpkt8WVPY/HjD8NwN0XZOOhS09O3turz+yAYf27olWzTNx0bkcc26oZZkjSEsrp2KYF/nptTxwRf0Adf9QRaN+6uWr5M088Gvdd1BVv3dlwfZ+L9x01suLn63FcawwbEOtbt57XCX+8vDtayRSgVJEMv6QbbuvTSfPcahAB1551Ai49tT0A4NJT2+OG3mnDGdefE9t29ZnH45peHQAAo2/spXjOxP06tlUztGnZFP+4+Sx88/BFyMwgdG3XKqXsh/H+e9xRzfHE1afi3M5t8MTVp2Lh45cBAD6N398HLj4ZRA0pIf92Y2pax/fu6ZPy+6lBpyXLXtHzONw/oBs+vb8ffinJtPWHgd3x+u29U47rfdIxmHx/Pwy/pBvOj2cem/rQRcn9rZs3wah4uxN13j+gG6bH+07HNrG0kUP6dQYAXOdxekSy+rEHEd0M4AohxH3x33cB6COEeFDtmJycHJGXl2epPoZhmMYKES0RQhhL6At7FnshAKl50BHAThvnYxiGYRzAjmL/CcApRNSFiJoBuBXAf50Ri2EYhrGK5Zl+IUQtEf0ewHcAMgGMF0KsdEwyhmEYxhK2lnCFEFMBTHVIFoZhGMYB+MtThmGYiMGKnWEYJmKwYmcYhokYrNgZhmEihuUPlCxVRlQKYKvFw9sBKHNQHD+IQhsScFuCS1TaE5V2APbb0lkIkWW0sKeK3Q5ElGfmy6sgEoU2JOC2BJeotCcq7QC8bwtPxTAMw0QMVuwMwzARI0yKfazfAjhAFNqQgNsSXKLSnqi0A/C4LaGZY2cYhmGMESaLnWEYhjGCEMKVfwDGAygBUCDZdhaABQBWAPgKwFHx7c0AvBvfvgzAxZJjzo1v3wDgNcTfMhTqUywHoD+AfAC1AG4KaRvuj29fCmAegNNDfD/uBlAab8tSAPeFuC0vS9qxDsC+kI+VzgBmAFgOYDaAjiFoywuIJfzZL9tuedy70BZFGU3cF9NtMd1YExelP4DesovyE4AB8b/vAfBc/O/hAN6N/90ewBIAGfHfiwH0Qyw72zcArlKpT7EcgGwAvQC8b/YGB6gNR0nKXAvg2xDfj7sBjIlC35KVeRCxCKehbQ+ATwEMif99KYAPQtCWvgA6IF2xZ8PiuHehLYoymrgvptvi2lSMEGIOgD2yzT0AzIn/PQ3AjfG/T0fMUoAQogTAPgA5RNQBMaW2QMRa+D6A6+R1aZUTQmwRQiwHUB/iNlRIiraCch7dULTFCQLaltsATAp5e5LnBjALFnIYe9mW+HELhRBFCtstj3sn26IloxSndZjXc+wFiFmcAHAzGjIwLQMwmIiaEFEXxF5JOiGWQ7VQcrxiXlUT5ZzAlzYQ0XAi2ghgNICHHGgH4N/9uJGIlhPRZCKylqQzHd/6FhF1BtAFwEybbZDiR3uWoUFRXQ+gNREda7MdgHtt8QOzbTGKo232WrHfA2A4ES0B0BrA4fj28Yg1JA/AKwDmIzafpJQ2XclaNVrOCXxpgxDidSFENwCPAXjKqvAy/GjLVwCyhRC9AEwH8J5V4WX42bduBTBZCFFnXmxV/GjPowAGENHPAAYA2BE/t13caosfmG2LURxts61EG2YRQqwB8EsAIKLuAAbFt9cC+GOiHBHNB7AewF7Ecqkm6AhgJxFlIjaHBcTS8b2pVC6ibfg4XjaUbRFC7JZsfxvA38LaFgm3IjbH6hg+3ZudAG6In/dIADcKIcqD2hYhxP/Zlc0sFtqiiOs6zMqigtF/iE36Sxce2sf/z0BsDume+O+WAFrF/x4IYI7kmJ8QW3xILChcrVKXZjkAE2BhESUIbQBwiqTMrwDkhfV+AOggKXM9gIVhbUt8Xw8AW6DitRGm9iAWqCqx4PcCgGeD3hZJecWFSVgc9062RU9Go2020xbLHdHABZkEoAhADWKvKPcCeBgxt7B1AEYlBkP84q0FsBqx1/POkvPkIDavtRHAGLUBpFYOwHnx+g8A2A1gZQjb8CqAlYi51c0C0DPE9+PFeFuWxdtyaljbEt/3DIBRERkrNyFmZa4DMA5A8xC0ZXS8nvr4/8/YHfcutEVRRhP3xXRb+MtThmGYiMFfnjIMw0QMVuwMwzARgxU7wzBMxGDFzjAMEzFYsTMMw0QMVuwMwzARgxU7wzBMxGDFzjAMEzH+H5/qPlTjLd0TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_tmy['Wspd'])#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "78cdd3bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ec9e664910>]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_tmy[df_tmy['Wspd']>6]['Wspd'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "87669563",
   "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>tracker_theta</th>\n",
       "      <th>aoi</th>\n",
       "      <th>surface_azimuth</th>\n",
       "      <th>surface_tilt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01 01:00:00-05:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 11:00:00-05:00</th>\n",
       "      <td>22.497707</td>\n",
       "      <td>60.686921</td>\n",
       "      <td>90.0</td>\n",
       "      <td>22.497707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04 23:00:00-05:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05 04:00:00-05:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05 11:00:00-05:00</th>\n",
       "      <td>22.324744</td>\n",
       "      <td>60.567332</td>\n",
       "      <td>90.0</td>\n",
       "      <td>22.324744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-28 22:00:00-05:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-28 23:00:00-05:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-29 00:00:00-05:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-29 12:00:00-05:00</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>51.658480</td>\n",
       "      <td>90.0</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-29 14:00:00-05:00</th>\n",
       "      <td>5.749148</td>\n",
       "      <td>60.861264</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.749148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>650 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           tracker_theta        aoi  surface_azimuth  \\\n",
       "1990-01-01 01:00:00-05:00            NaN        NaN              NaN   \n",
       "1990-01-01 11:00:00-05:00      22.497707  60.686921             90.0   \n",
       "1990-01-04 23:00:00-05:00            NaN        NaN              NaN   \n",
       "1990-01-05 04:00:00-05:00            NaN        NaN              NaN   \n",
       "1990-01-05 11:00:00-05:00      22.324744  60.567332             90.0   \n",
       "...                                  ...        ...              ...   \n",
       "1990-12-28 22:00:00-05:00            NaN        NaN              NaN   \n",
       "1990-12-28 23:00:00-05:00            NaN        NaN              NaN   \n",
       "1990-12-29 00:00:00-05:00            NaN        NaN              NaN   \n",
       "1990-12-29 12:00:00-05:00      50.000000  51.658480             90.0   \n",
       "1990-12-29 14:00:00-05:00       5.749148  60.861264             90.0   \n",
       "\n",
       "                           surface_tilt  \n",
       "1990-01-01 01:00:00-05:00           NaN  \n",
       "1990-01-01 11:00:00-05:00     22.497707  \n",
       "1990-01-04 23:00:00-05:00           NaN  \n",
       "1990-01-05 04:00:00-05:00           NaN  \n",
       "1990-01-05 11:00:00-05:00     22.324744  \n",
       "...                                 ...  \n",
       "1990-12-28 22:00:00-05:00           NaN  \n",
       "1990-12-28 23:00:00-05:00           NaN  \n",
       "1990-12-29 00:00:00-05:00           NaN  \n",
       "1990-12-29 12:00:00-05:00     50.000000  \n",
       "1990-12-29 14:00:00-05:00      5.749148  \n",
       "\n",
       "[650 rows x 4 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracking_angles[df_tmy['Wspd']>6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "41ad0ba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "smart_trackingdata=tracking_angles.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "d19a7a9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "1a67dbaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "smart_trackingdata['tracker_theta'] = np.where(df_tmy['Wspd'] > 3, 0, smart_trackingdata['tracker_theta'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "dd9d3a06",
   "metadata": {},
   "outputs": [],
   "source": [
    "#tracking_angles.iloc[df_tmy['Wspd']>6]['tracker_theta']=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "a88941a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990-01-01 01:00:00-05:00          NaN\n",
       "1990-01-01 11:00:00-05:00    22.497707\n",
       "1990-01-04 23:00:00-05:00          NaN\n",
       "1990-01-05 04:00:00-05:00          NaN\n",
       "1990-01-05 11:00:00-05:00    22.324744\n",
       "                               ...    \n",
       "1990-12-28 22:00:00-05:00          NaN\n",
       "1990-12-28 23:00:00-05:00          NaN\n",
       "1990-12-29 00:00:00-05:00          NaN\n",
       "1990-12-29 12:00:00-05:00    50.000000\n",
       "1990-12-29 14:00:00-05:00     5.749148\n",
       "Name: tracker_theta, Length: 650, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracking_angles[df_tmy['Wspd']>6]['tracker_theta']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "4aff8f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990-01-01 01:00:00-05:00    0.0\n",
       "1990-01-01 11:00:00-05:00    0.0\n",
       "1990-01-04 23:00:00-05:00    0.0\n",
       "1990-01-05 04:00:00-05:00    0.0\n",
       "1990-01-05 11:00:00-05:00    0.0\n",
       "                            ... \n",
       "1990-12-28 22:00:00-05:00    0.0\n",
       "1990-12-28 23:00:00-05:00    0.0\n",
       "1990-12-29 00:00:00-05:00    0.0\n",
       "1990-12-29 12:00:00-05:00    0.0\n",
       "1990-12-29 14:00:00-05:00    0.0\n",
       "Name: tracker_theta, Length: 650, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smart_trackingdata[df_tmy['Wspd']>6]['tracker_theta']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "aa968776",
   "metadata": {},
   "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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmy.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "5080cb12",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ec9e732c70>]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(smart_trackingdata['surface_tilt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "8e7e6a29",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_polymer']\n",
    "cell_temperature = pvlib.temperature.sapm_cell(array_poa,\n",
    "                                               df_tmy['DryBulb'],\n",
    "                                               df_tmy['Wspd'],\n",
    "                                               **parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "93484bb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "array_poa = pvlib.irradiance.get_total_irradiance(surface_tilt=tracking_angles['tracker_theta'],\n",
    "                                                       surface_azimuth=tracking_angles['surface_azimuth'],\n",
    "                                                       dni=df_tmy['DNI'],\n",
    "                                                       ghi=df_tmy['GHI'],\n",
    "                                                       dhi=df_tmy['DHI'],\n",
    "                                                       solar_zenith=solar_position['apparent_zenith'],\n",
    "                                                       solar_azimuth=solar_position['azimuth'])\n",
    "array_poa = array_poa['poa_global']  # Returns various irradiances; we will use the global value at plane of array.\n",
    "\n",
    "parameters = pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_polymer']\n",
    "cell_temperature = pvlib.temperature.sapm_cell(array_poa,\n",
    "                                               df_tmy['DryBulb'],\n",
    "                                               df_tmy['Wspd'],\n",
    "                                               **parameters)\n",
    "\n",
    "gamma_pdc = -0.004  # divide by 100 to go from %/°C to 1/°C\n",
    "nameplate = 1000 # Watts\n",
    "\n",
    "array_power = pvlib.pvsystem.pvwatts_dc(array_poa, cell_temperature, nameplate, gamma_pdc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6f26b0e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "array_poa2 = pvlib.irradiance.get_total_irradiance(surface_tilt=smart_trackingdata['tracker_theta'],\n",
    "                                                       surface_azimuth=smart_trackingdata['surface_azimuth'],\n",
    "                                                       dni=df_tmy['DNI'],\n",
    "                                                       ghi=df_tmy['GHI'],\n",
    "                                                       dhi=df_tmy['DHI'],\n",
    "                                                       solar_zenith=solar_position['apparent_zenith'],\n",
    "                                                       solar_azimuth=solar_position['azimuth'])\n",
    "array_poa2 = array_poa2['poa_global']  # Returns various irradiances; we will use the global value at plane of array.\n",
    "\n",
    "\n",
    "gamma_pdc = -0.004  # divide by 100 to go from %/°C to 1/°C\n",
    "nameplate = 1000 # Watts\n",
    "\n",
    "\n",
    "parameters = pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_polymer']\n",
    "cell_temperature2 = pvlib.temperature.sapm_cell(array_poa2,\n",
    "                                               df_tmy['DryBulb'],\n",
    "                                               df_tmy['Wspd'],\n",
    "                                               **parameters)\n",
    "\n",
    "\n",
    "\n",
    "array_power2 = pvlib.pvsystem.pvwatts_dc(array_poa2, cell_temperature2, nameplate, gamma_pdc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "6eab5d73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3747064389180358"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(array_power2.sum()-array_power.sum())*100/array_power.sum()"
   ]
  }
 ],
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