{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LETID - Outdoor Scenario Based on Accelerated Test\n",
    "\n",
    "This is an example for using a test result to model LETID progression in outdoor environments\n",
    "\n",
    "One important use case for this library is to use data from a LETID test (e.g. [IEC TS 63342](https://webstore.iec.ch/publication/67332)) to model how a module may degrade and regenerate in the field.\n",
    "\n",
    "We will take some data from module testing presented in [Karas *et al.* 2022](https://onlinelibrary.wiley.com/doi/10.1002/pip.3573), and use it to estimate device parameters.\n",
    "\n",
    "We can use the equations in this library to model LETID progression in a simulated outdoor environment, given that we have weather and system data. This example makes use of tools from the fabulous [pvlib](https://pvlib-python.readthedocs.io/en/stable/) library to calculate system irradiance and temperature, which we use to calculate progression in LETID states.\n",
    "\n",
    "\n",
    "\n",
    "**Requirements:**\n",
    "- `pvlib`, `pandas`, `numpy`, `matplotlib`, `scipy`\n",
    "\n",
    "**Objectives:**\n",
    "1. Load data from example test results\n",
    "2. Use `pvlib` and provided weather files to set up a temperature and injection timeseries\n",
    "3. Define necessary solar cell device parameters\n",
    "4. Define necessary degradation parameters: degraded lifetime and defect states\n",
    "5. Run through timeseries, calculating defect states\n",
    "6. Calculate device degradation and plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n",
    "# !pip install pvdeg==0.3.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pvdeg import letid, collection, utilities, DATA_DIR\n",
    "\n",
    "import pvdeg\n",
    "import pvlib\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on a  Windows 10\n",
      "Python version  3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n",
      "Pandas version  2.2.0\n",
      "pvdeg version  0.2.4.dev83+ge2ceab9.d20240422\n"
     ]
    }
   ],
   "source": [
    "# This information helps with debugging and getting support :)\n",
    "import sys, platform\n",
    "print(\"Working on a \", platform.system(), platform.release())\n",
    "print(\"Python version \", sys.version)\n",
    "print(\"Pandas version \", pd.__version__)\n",
    "print(\"pvdeg version \", pvdeg.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll load some data taken from an accelerated test. See [Karas *et al.* 2022](https://onlinelibrary.wiley.com/doi/10.1002/pip.3573) for full details. This data is the average of \"Type S\" modules from Lab 3. Type S modules were prototype modules made with 48 monocrystalline cells, and degraded about 4-5% in LETID testing. Data throughout testing is shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_area = 243 # cm^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Week</th>\n",
       "      <th>Pmp</th>\n",
       "      <th>Isc</th>\n",
       "      <th>Voc</th>\n",
       "      <th>FF</th>\n",
       "      <th>cell Voc</th>\n",
       "      <th>Jsc</th>\n",
       "      <th>% degradation</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>236.7</td>\n",
       "      <td>9.91</td>\n",
       "      <td>31.86</td>\n",
       "      <td>75.01</td>\n",
       "      <td>0.663750</td>\n",
       "      <td>40.781893</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>9.82</td>\n",
       "      <td>31.63</td>\n",
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       "      <td>0.658958</td>\n",
       "      <td>40.411523</td>\n",
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       "      <td>31.54</td>\n",
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       "      <td>0.657083</td>\n",
       "      <td>40.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>226.9</td>\n",
       "      <td>9.70</td>\n",
       "      <td>31.56</td>\n",
       "      <td>74.10</td>\n",
       "      <td>0.657500</td>\n",
       "      <td>39.917695</td>\n",
       "      <td>-4.319083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>224.8</td>\n",
       "      <td>9.62</td>\n",
       "      <td>31.48</td>\n",
       "      <td>74.22</td>\n",
       "      <td>0.655833</td>\n",
       "      <td>39.588477</td>\n",
       "      <td>-5.293594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>235.8</td>\n",
       "      <td>9.76</td>\n",
       "      <td>31.98</td>\n",
       "      <td>75.51</td>\n",
       "      <td>0.666250</td>\n",
       "      <td>40.164609</td>\n",
       "      <td>-0.381679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Week    Pmp   Isc    Voc     FF  cell Voc        Jsc  % degradation\n",
       "0     0  236.7  9.91  31.86  75.01  0.663750  40.781893       0.000000\n",
       "1     1  230.9  9.82  31.63  74.38  0.658958  40.411523      -2.511910\n",
       "2     2  228.5  9.72  31.54  74.50  0.657083  40.000000      -3.588621\n",
       "3     3  226.9  9.70  31.56  74.10  0.657500  39.917695      -4.319083\n",
       "4     4  224.8  9.62  31.48  74.22  0.655833  39.588477      -5.293594\n",
       "5     7  235.8  9.76  31.98  75.51  0.666250  40.164609      -0.381679"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(os.path.join(DATA_DIR, 'module test data.csv'))\n",
    "df['cell Voc'] = df['Voc']/48\n",
    "df['Jsc'] = df['Isc']/cell_area*1000\n",
    "df['% degradation'] = (df['Pmp']-df['Pmp'].iloc[0])/df['Pmp']*100\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The module parameter that is most sensitve to our device input parameters is cell open-circuit voltage, which in this case started at about 0.664 V/cell. We will select reasonable values for solar cell input parameters, and use ```letid.calc_voc_from_tau()``` to check if those parameters match the cell Voc of the device we're trying to model. The important quantities here are bulk lifetime in the initial state (```tau_0```), wafer thickness, and rear surface recombination velocity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6611301062313933"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tau_0 = 120 # [us] reasonable bulk lifetime for commercial-quality p-type silicon, maybe a little on the low side for typical wafer lifetimes, which could range from 100-500 us.\n",
    "\n",
    "wafer_thickness = 180 # [um] a reasonable wafer thickness for typical commercial silicon solar cells. Wafer thicknesses for Si solar cells are typically 160-180 um.\n",
    "\n",
    "srv_rear = 100 # [cm/s] a reasonable value for rear surface recombination velocity for passivated emitter and rear cell (PERC) solar cells.\n",
    "# Typical PERC cells might have a rear SRV anywhere from 50 to 100 cm/s. Higher values for early PERC, lower values for more modern, higher efficiency PERC.\n",
    "# Other device structures will have different SRV values. E.g., aluminum back surface field (Al-BSF) cells could be ~500 cm/s. TopCON or other high efficiency cell structures will be lower, e.g. 10 cm/s.\n",
    "# Note that all of these values are intepreted as \"lumped\" values for the entire rear surface.\n",
    "\n",
    "isc_0 = df.query(\"Week == 0\")['Isc'].item() # [A] we'll use the short circuit current from the Week 0 test data, instead of trying to calculate it\n",
    "cell_area = 240.8 # [cm^2] typical cell size for 6-inch pseudosquare monocrystalline silicon  wafers\n",
    "jsc_0 = isc_0/cell_area*1000 # [mA/cm^2] short circuit current density\n",
    "\n",
    "temperature = 25 # [C] standard measurement temperature\n",
    "\n",
    "voc_0 = letid.calc_voc_from_tau(tau_0, wafer_thickness, srv_rear, jsc_0, temperature)\n",
    "voc_0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "generation_df = pd.read_excel(os.path.join(DATA_DIR, 'PVL_GenProfile.xlsx'), header = 0) # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n",
    "generation = generation_df['Generation (cm-3s-1)']\n",
    "depth = generation_df['Depth (um)']\n",
    "\n",
    "d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty close!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(235.89622533600513, 236.7)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check to make sure power is close to the measured Week 0 power\n",
    "\n",
    "ff_0 = df.query(\"Week == 0\")['FF'].item()# [%] fill factor\n",
    "\n",
    "pmp_0 = (voc_0*48)*(jsc_0*cell_area/1000)*ff_0/100 # [W] maximum power\n",
    "pmp_0, df.query(\"Week == 0\")['Pmp'].item()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we do the same thing for the degraded state to determine ```tau_deg```, the bulk lifetime when the module is in its most degraded state. So here the cell Voc target is the roughly 0.656 V measured after 4 weeks of testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6538686361228122"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tau_deg = 80 # [us] degraded bulk lifetime\n",
    "\n",
    "isc_deg = df.query(\"Week == 4\")['Isc'].item() # [A] we'll use the short circuit current from the Week 4 test data, instead of trying to calculate it\n",
    "jsc_deg = isc_deg/cell_area*1000 # [mA/cm^2] short circuit current density\n",
    "\n",
    "voc_deg = letid.calc_voc_from_tau(tau_deg, wafer_thickness, srv_rear, jsc_deg, temperature)\n",
    "voc_deg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(224.09272908700694, 224.8)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check to make sure power is close to the measured Week 4 power\n",
    "\n",
    "ff_deg = df.query(\"Week == 4\")['FF'].item()# [%] fill factor\n",
    "\n",
    "pmp_deg = (voc_deg*48)*(jsc_deg*cell_area/1000)*ff_deg/100 # [W] maximum power\n",
    "pmp_deg, df.query(\"Week == 4\")['Pmp'].item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.050036816961295416"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pmp_0 - pmp_deg) / pmp_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So for modeling this module, we will use ```tau_0``` = 120 $\\mu s$, ```tau_deg``` = 80 $\\mu s$, with ```wafer_thickness``` = 180 $\\mu m$ and ```srv_rear``` = 100 cm/s.\n",
    "\n",
    "Great!\n",
    "\n",
    "The example proceeds below in similar fashion to the outdoor example, using a fixed latitude tilt system at NREL, in Golden, CO, USA, using [NSRDB](https://nsrdb.nrel.gov/) hourly PSM weather data, SAPM temperature models, and module and inverter models from the CEC database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load weather and location data, use pvlib read_psm3 function\n",
    "\n",
    "sam_file = 'psm3.csv'\n",
    "weather, meta = pvdeg.weather.read(os.path.join(DATA_DIR, sam_file), file_type='PSM3', map_variables = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1999-12-31 20:30:00-07:00</th>\n",
       "      <td>1999</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>86.82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-31 21:30:00-07:00</th>\n",
       "      <td>1999</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>83.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-31 22:30:00-07:00</th>\n",
       "      <td>1999</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>81.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-31 23:30:00-07:00</th>\n",
       "      <td>1999</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>79.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Year  Month  Day  Hour  Minute  dni  dhi  ghi  \\\n",
       "1999-01-01 00:30:00-07:00  1999      1    1     0      30  0.0  0.0  0.0   \n",
       "1999-01-01 01:30:00-07:00  1999      1    1     1      30  0.0  0.0  0.0   \n",
       "1999-01-01 02:30:00-07:00  1999      1    1     2      30  0.0  0.0  0.0   \n",
       "1999-01-01 03:30:00-07:00  1999      1    1     3      30  0.0  0.0  0.0   \n",
       "1999-01-01 04:30:00-07:00  1999      1    1     4      30  0.0  0.0  0.0   \n",
       "...                         ...    ...  ...   ...     ...  ...  ...  ...   \n",
       "1999-12-31 19:30:00-07:00  1999     12   31    19      30  0.0  0.0  0.0   \n",
       "1999-12-31 20:30:00-07:00  1999     12   31    20      30  0.0  0.0  0.0   \n",
       "1999-12-31 21:30:00-07:00  1999     12   31    21      30  0.0  0.0  0.0   \n",
       "1999-12-31 22:30:00-07:00  1999     12   31    22      30  0.0  0.0  0.0   \n",
       "1999-12-31 23:30:00-07:00  1999     12   31    23      30  0.0  0.0  0.0   \n",
       "\n",
       "                           temp_air  dew_point  wind_speed  relative_humidity  \\\n",
       "1999-01-01 00:30:00-07:00       0.0       -5.0         1.8              79.39   \n",
       "1999-01-01 01:30:00-07:00       0.0       -4.0         1.7              80.84   \n",
       "1999-01-01 02:30:00-07:00       0.0       -4.0         1.5              82.98   \n",
       "1999-01-01 03:30:00-07:00       0.0       -4.0         1.3              85.01   \n",
       "1999-01-01 04:30:00-07:00       0.0       -4.0         1.3              85.81   \n",
       "...                             ...        ...         ...                ...   \n",
       "1999-12-31 19:30:00-07:00       0.0       -3.0         0.9              83.63   \n",
       "1999-12-31 20:30:00-07:00       0.0       -3.0         1.2              86.82   \n",
       "1999-12-31 21:30:00-07:00       0.0       -4.0         1.6              83.78   \n",
       "1999-12-31 22:30:00-07:00       0.0       -4.0         1.7              81.22   \n",
       "1999-12-31 23:30:00-07:00       0.0       -5.0         1.8              79.43   \n",
       "\n",
       "                           poa_global  temp_cell  temp_module  \n",
       "1999-01-01 00:30:00-07:00         0.0        0.0          0.0  \n",
       "1999-01-01 01:30:00-07:00         0.0        0.0          0.0  \n",
       "1999-01-01 02:30:00-07:00         0.0        0.0          0.0  \n",
       "1999-01-01 03:30:00-07:00         0.0        0.0          0.0  \n",
       "1999-01-01 04:30:00-07:00         0.0        0.0          0.0  \n",
       "...                               ...        ...          ...  \n",
       "1999-12-31 19:30:00-07:00         0.0        0.0          0.0  \n",
       "1999-12-31 20:30:00-07:00         0.0        0.0          0.0  \n",
       "1999-12-31 21:30:00-07:00         0.0        0.0          0.0  \n",
       "1999-12-31 22:30:00-07:00         0.0        0.0          0.0  \n",
       "1999-12-31 23:30:00-07:00         0.0        0.0          0.0  \n",
       "\n",
       "[8760 rows x 15 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#if our weather file doesn't have precipitable water, calculate it with pvlib\n",
    "if not 'precipitable_water' in weather.columns:\n",
    "    weather['precipitable_water'] = pvlib.atmosphere.gueymard94_pw(weather['temp_air'], weather['relative_humidity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop unneeded columns\n",
    "weather = weather[['ghi', 'dni', 'dhi', 'temp_air', 'wind_speed', 'relative_humidity', 'precipitable_water']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ghi</th>\n",
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       "      <th>1999-01-01 01:30:00-07:00</th>\n",
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       "      <th>1999-01-01 02:30:00-07:00</th>\n",
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       "    <tr>\n",
       "      <th>1999-01-01 03:30:00-07:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>85.01</td>\n",
       "      <td>0.955004</td>\n",
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       "    <tr>\n",
       "      <th>1999-01-01 04:30:00-07:00</th>\n",
       "      <td>0.0</td>\n",
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       "      <th>1999-12-31 19:30:00-07:00</th>\n",
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       "      <th>1999-12-31 20:30:00-07:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>86.82</td>\n",
       "      <td>0.975338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-31 21:30:00-07:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>83.78</td>\n",
       "      <td>0.941186</td>\n",
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       "    <tr>\n",
       "      <th>1999-12-31 22:30:00-07:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>81.22</td>\n",
       "      <td>0.912427</td>\n",
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       "    <tr>\n",
       "      <th>1999-12-31 23:30:00-07:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>79.43</td>\n",
       "      <td>0.892318</td>\n",
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       "<p>8760 rows × 7 columns</p>\n",
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      "text/plain": [
       "                           ghi  dni  dhi  temp_air  wind_speed  \\\n",
       "1999-01-01 00:30:00-07:00  0.0  0.0  0.0       0.0         1.8   \n",
       "1999-01-01 01:30:00-07:00  0.0  0.0  0.0       0.0         1.7   \n",
       "1999-01-01 02:30:00-07:00  0.0  0.0  0.0       0.0         1.5   \n",
       "1999-01-01 03:30:00-07:00  0.0  0.0  0.0       0.0         1.3   \n",
       "1999-01-01 04:30:00-07:00  0.0  0.0  0.0       0.0         1.3   \n",
       "...                        ...  ...  ...       ...         ...   \n",
       "1999-12-31 19:30:00-07:00  0.0  0.0  0.0       0.0         0.9   \n",
       "1999-12-31 20:30:00-07:00  0.0  0.0  0.0       0.0         1.2   \n",
       "1999-12-31 21:30:00-07:00  0.0  0.0  0.0       0.0         1.6   \n",
       "1999-12-31 22:30:00-07:00  0.0  0.0  0.0       0.0         1.7   \n",
       "1999-12-31 23:30:00-07:00  0.0  0.0  0.0       0.0         1.8   \n",
       "\n",
       "                           relative_humidity  precipitable_water  \n",
       "1999-01-01 00:30:00-07:00              79.39            0.891869  \n",
       "1999-01-01 01:30:00-07:00              80.84            0.908158  \n",
       "1999-01-01 02:30:00-07:00              82.98            0.932199  \n",
       "1999-01-01 03:30:00-07:00              85.01            0.955004  \n",
       "1999-01-01 04:30:00-07:00              85.81            0.963991  \n",
       "...                                      ...                 ...  \n",
       "1999-12-31 19:30:00-07:00              83.63            0.939501  \n",
       "1999-12-31 20:30:00-07:00              86.82            0.975338  \n",
       "1999-12-31 21:30:00-07:00              83.78            0.941186  \n",
       "1999-12-31 22:30:00-07:00              81.22            0.912427  \n",
       "1999-12-31 23:30:00-07:00              79.43            0.892318  \n",
       "\n",
       "[8760 rows x 7 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up PVlib model\n",
    "Note that the module we select here is NOT the same \"Type S\" module that was tested for LETID. I'm simply trying to find a module in the CEC database with I-V characteristics that are reasonably close to the tested module, so the pvlib calculated DC results are close to how our Type S module might behave in the field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.307502</td>\n",
       "      <td>314.551514</td>\n",
       "      <td>2.112491</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>N</td>\n",
       "      <td>SAM 2018.11.11 r2</td>\n",
       "      <td>1/3/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LG_Electronics_Inc__LG235N8K_G4</th>\n",
       "      <td>Mono-c-Si</td>\n",
       "      <td>0</td>\n",
       "      <td>235.216</td>\n",
       "      <td>217.6</td>\n",
       "      <td>1.274</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.98</td>\n",
       "      <td>48</td>\n",
       "      <td>9.96</td>\n",
       "      <td>30.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.124688</td>\n",
       "      <td>10.375943</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.278936</td>\n",
       "      <td>250.799469</td>\n",
       "      <td>3.374386</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>N</td>\n",
       "      <td>SAM 2018.11.11 r2</td>\n",
       "      <td>1/3/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LG_Electronics_Inc__LG240N8K_G4</th>\n",
       "      <td>Mono-c-Si</td>\n",
       "      <td>0</td>\n",
       "      <td>240.1</td>\n",
       "      <td>222.4</td>\n",
       "      <td>1.274</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.98</td>\n",
       "      <td>48</td>\n",
       "      <td>10.01</td>\n",
       "      <td>30.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.134983</td>\n",
       "      <td>10.313996</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.263453</td>\n",
       "      <td>3976.513916</td>\n",
       "      <td>3.244429</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>N</td>\n",
       "      <td>SAM 2018.11.11 r2</td>\n",
       "      <td>1/3/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LG_Electronics_Inc__LG245N8K_G4</th>\n",
       "      <td>Mono-c-Si</td>\n",
       "      <td>0</td>\n",
       "      <td>245.016</td>\n",
       "      <td>227.1</td>\n",
       "      <td>1.274</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.98</td>\n",
       "      <td>48</td>\n",
       "      <td>10.06</td>\n",
       "      <td>30.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1.150454</td>\n",
       "      <td>10.36712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.246041</td>\n",
       "      <td>1112.830811</td>\n",
       "      <td>4.077489</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>N</td>\n",
       "      <td>SAM 2018.11.11 r2</td>\n",
       "      <td>1/3/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prism_Solar_Technologies_Bi48_279BSTC</th>\n",
       "      <td>Mono-c-Si</td>\n",
       "      <td>1</td>\n",
       "      <td>220.472</td>\n",
       "      <td>199.3</td>\n",
       "      <td>1.668</td>\n",
       "      <td>1.695</td>\n",
       "      <td>0.984</td>\n",
       "      <td>48</td>\n",
       "      <td>9.15</td>\n",
       "      <td>31.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.266369</td>\n",
       "      <td>9.15029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.210912</td>\n",
       "      <td>6628.005371</td>\n",
       "      <td>13.999823</td>\n",
       "      <td>-0.4399</td>\n",
       "      <td>N</td>\n",
       "      <td>SAM 2018.11.11 r2</td>\n",
       "      <td>1/3/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prism_Solar_Technologies_Bi48_286BSTC</th>\n",
       "      <td>Mono-c-Si</td>\n",
       "      <td>1</td>\n",
       "      <td>225.298</td>\n",
       "      <td>204.0</td>\n",
       "      <td>1.668</td>\n",
       "      <td>1.695</td>\n",
       "      <td>0.984</td>\n",
       "      <td>48</td>\n",
       "      <td>9.35</td>\n",
       "      <td>31.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.266347</td>\n",
       "      <td>9.350275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.206403</td>\n",
       "      <td>7016.816406</td>\n",
       "      <td>13.996228</td>\n",
       "      <td>-0.4399</td>\n",
       "      <td>N</td>\n",
       "      <td>SAM 2018.11.11 r2</td>\n",
       "      <td>1/3/2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Technology Bifacial      STC    PTC  \\\n",
       "Gintech_Energy_GIN_M6_48_220            Mono-c-Si        0   220.48  194.5   \n",
       "Gintech_Energy_GIN_P6_48_220           Multi-c-Si        0  220.378  199.2   \n",
       "Gintech_Energy_GIN_M6_48_225            Mono-c-Si        0  225.844  199.1   \n",
       "LG_Electronics_Inc__LG230N8K_G4         Mono-c-Si        0  230.127  212.9   \n",
       "LG_Electronics_Inc__LG235N8K_G4         Mono-c-Si        0  235.216  217.6   \n",
       "LG_Electronics_Inc__LG240N8K_G4         Mono-c-Si        0    240.1  222.4   \n",
       "LG_Electronics_Inc__LG245N8K_G4         Mono-c-Si        0  245.016  227.1   \n",
       "Prism_Solar_Technologies_Bi48_279BSTC   Mono-c-Si        1  220.472  199.3   \n",
       "Prism_Solar_Technologies_Bi48_286BSTC   Mono-c-Si        1  225.298  204.0   \n",
       "\n",
       "                                         A_c Length  Width N_s I_sc_ref  \\\n",
       "Gintech_Energy_GIN_M6_48_220           1.313  1.324  0.992  48     9.31   \n",
       "Gintech_Energy_GIN_P6_48_220           1.313  1.324  0.992  48     9.28   \n",
       "Gintech_Energy_GIN_M6_48_225           1.313  1.324  0.992  48     9.45   \n",
       "LG_Electronics_Inc__LG230N8K_G4        1.274    1.3   0.98  48      9.9   \n",
       "LG_Electronics_Inc__LG235N8K_G4        1.274    1.3   0.98  48     9.96   \n",
       "LG_Electronics_Inc__LG240N8K_G4        1.274    1.3   0.98  48    10.01   \n",
       "LG_Electronics_Inc__LG245N8K_G4        1.274    1.3   0.98  48    10.06   \n",
       "Prism_Solar_Technologies_Bi48_279BSTC  1.668  1.695  0.984  48     9.15   \n",
       "Prism_Solar_Technologies_Bi48_286BSTC  1.668  1.695  0.984  48     9.35   \n",
       "\n",
       "                                      V_oc_ref  ...     a_ref    I_L_ref  \\\n",
       "Gintech_Energy_GIN_M6_48_220              31.3  ...  1.521151     9.3228   \n",
       "Gintech_Energy_GIN_P6_48_220              31.3  ...  1.379815   9.379174   \n",
       "Gintech_Energy_GIN_M6_48_225              31.5  ...  1.530596   9.461989   \n",
       "LG_Electronics_Inc__LG230N8K_G4           30.0  ...  1.110894  10.312051   \n",
       "LG_Electronics_Inc__LG235N8K_G4           30.2  ...  1.124688  10.375943   \n",
       "LG_Electronics_Inc__LG240N8K_G4           30.5  ...  1.134983  10.313996   \n",
       "LG_Electronics_Inc__LG245N8K_G4           30.8  ...  1.150454   10.36712   \n",
       "Prism_Solar_Technologies_Bi48_279BSTC     31.0  ...  1.266369    9.15029   \n",
       "Prism_Solar_Technologies_Bi48_286BSTC     31.0  ...  1.266347   9.350275   \n",
       "\n",
       "                                      I_o_ref       R_s     R_sh_ref  \\\n",
       "Gintech_Energy_GIN_M6_48_220              0.0  0.106087    77.164063   \n",
       "Gintech_Energy_GIN_P6_48_220              0.0  0.254869   374.724518   \n",
       "Gintech_Energy_GIN_M6_48_225              0.0  0.100905    79.531746   \n",
       "LG_Electronics_Inc__LG230N8K_G4           0.0  0.307502   314.551514   \n",
       "LG_Electronics_Inc__LG235N8K_G4           0.0  0.278936   250.799469   \n",
       "LG_Electronics_Inc__LG240N8K_G4           0.0  0.263453  3976.513916   \n",
       "LG_Electronics_Inc__LG245N8K_G4           0.0  0.246041  1112.830811   \n",
       "Prism_Solar_Technologies_Bi48_279BSTC     0.0  0.210912  6628.005371   \n",
       "Prism_Solar_Technologies_Bi48_286BSTC     0.0  0.206403  7016.816406   \n",
       "\n",
       "                                          Adjust gamma_r BIPV  \\\n",
       "Gintech_Energy_GIN_M6_48_220           22.488531   -0.55    N   \n",
       "Gintech_Energy_GIN_P6_48_220            8.824213   -0.49    N   \n",
       "Gintech_Energy_GIN_M6_48_225           22.462965   -0.55    N   \n",
       "LG_Electronics_Inc__LG230N8K_G4         2.112491   -0.38    N   \n",
       "LG_Electronics_Inc__LG235N8K_G4         3.374386   -0.38    N   \n",
       "LG_Electronics_Inc__LG240N8K_G4         3.244429   -0.38    N   \n",
       "LG_Electronics_Inc__LG245N8K_G4         4.077489   -0.38    N   \n",
       "Prism_Solar_Technologies_Bi48_279BSTC  13.999823 -0.4399    N   \n",
       "Prism_Solar_Technologies_Bi48_286BSTC  13.996228 -0.4399    N   \n",
       "\n",
       "                                                 Version      Date  \n",
       "Gintech_Energy_GIN_M6_48_220           SAM 2018.11.11 r2  1/3/2019  \n",
       "Gintech_Energy_GIN_P6_48_220           SAM 2018.11.11 r2  1/3/2019  \n",
       "Gintech_Energy_GIN_M6_48_225           SAM 2018.11.11 r2  1/3/2019  \n",
       "LG_Electronics_Inc__LG230N8K_G4        SAM 2018.11.11 r2  1/3/2019  \n",
       "LG_Electronics_Inc__LG235N8K_G4        SAM 2018.11.11 r2  1/3/2019  \n",
       "LG_Electronics_Inc__LG240N8K_G4        SAM 2018.11.11 r2  1/3/2019  \n",
       "LG_Electronics_Inc__LG245N8K_G4        SAM 2018.11.11 r2  1/3/2019  \n",
       "Prism_Solar_Technologies_Bi48_279BSTC  SAM 2018.11.11 r2  1/3/2019  \n",
       "Prism_Solar_Technologies_Bi48_286BSTC  SAM 2018.11.11 r2  1/3/2019  \n",
       "\n",
       "[9 rows x 25 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cec_modules = pvlib.pvsystem.retrieve_sam('CECMod').T\n",
    "cec_modules[cec_modules['STC'].between(220, 250) & (cec_modules['N_s'] == 48)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The LG ones look close to the module we're trying to model. Pmp around 235W, Isc around 9.9A. Let's go with 'LG_Electronics_Inc__LG235N8K_G4'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "cec_modules = cec_modules.T\n",
    "cec_module = cec_modules['LG_Electronics_Inc__LG235N8K_G4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the rest of the pvlib stuff\n",
    "# we'll use the SAPM temperature model open-rack glass/polymer coeffecients.\n",
    "\n",
    "from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS\n",
    "from pvlib.location import Location\n",
    "from pvlib.pvsystem import PVSystem\n",
    "from pvlib.modelchain import ModelChain\n",
    "\n",
    "cec_inverters = pvlib.pvsystem.retrieve_sam('cecinverter')\n",
    "cec_inverter = cec_inverters['ABB__ULTRA_750_TL_OUTD_1_US_690_x_y_z__690V_']\n",
    "\n",
    "temperature_model_parameters = TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_polymer']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up system in pvlib\n",
    "lat = meta['latitude']\n",
    "lon = meta['longitude']\n",
    "tz = meta['tz']\n",
    "elevation = meta['altitude']\n",
    "surface_tilt = lat # fixed, latitude tilt\n",
    "surface_azimuth = 180 # south-facing\n",
    "\n",
    "location = Location(lat, lon, tz, elevation, 'Golden, CO, USA')\n",
    "\n",
    "system = PVSystem(surface_tilt = surface_tilt, surface_azimuth = surface_azimuth,\n",
    "                  module_parameters = cec_module,\n",
    "                  inverter_parameters = cec_inverter,\n",
    "                  temperature_model_parameters = temperature_model_parameters,\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModelChain: \n",
       "  name: None\n",
       "  clearsky_model: ineichen\n",
       "  transposition_model: haydavies\n",
       "  solar_position_method: nrel_numpy\n",
       "  airmass_model: kastenyoung1989\n",
       "  dc_model: cec\n",
       "  ac_model: sandia_inverter\n",
       "  aoi_model: physical_aoi_loss\n",
       "  spectral_model: first_solar_spectral_loss\n",
       "  temperature_model: sapm_temp\n",
       "  losses_model: no_extra_losses"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create and run pvlib modelchain\n",
    "mc = ModelChain(system, location, aoi_model = \"physical\")\n",
    "mc.run_model(weather)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up timeseries\n",
    "In this example, injection is a function of both the operating point of the module (which we will assume is maximum power point) and irradiance. Maximum power point injection is equivalent to $(I_{sc}-I_{mp})/I_{sc}\\times Ee$, where $Ee$ is effective irradiance, the irradiance absorbed by the module's cells. We normalize it to 1-sun irradiance, 1000 $W/m^2$.\n",
    "\n",
    "We will use the irradiance, DC operating point, and cell temperature from the pvlib modelchain results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "ee = mc.results.effective_irradiance\n",
    "#injection = (mc.results.dc['i_sc']-mc.results.dc['i_mp'])/(mc.results.dc['i_sc'])*(ee/1000)\n",
    "injection = letid.calc_injection_outdoors(mc.results)\n",
    "temperature = mc.results.cell_temperature\n",
    "\n",
    "timesteps = pd.DataFrame({'Temperature':temperature, 'Injection':injection}) # create a DataFrame with cell temperature and injection\n",
    "timesteps.reset_index(inplace = True) # reset the index so datetime is a column. I prefer integer indexing.\n",
    "timesteps.rename(columns = {'index':'Datetime'}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "##### Remaining degradation parameters:\n",
    "We've already set our important device parameters: ```tau_0```, ```tau_deg```, ```wafer_thickness```, ```srv_rear```, ```cell_area```, etc, but we need a few more: generation profile and carrier diffusivity. These are necessary for calculating current collection, and the \"default\" values provided here should be sufficient for most use cases.\n",
    "\n",
    "The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters.\n",
    "\n",
    "In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the parameters for LETID degradation from Repins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "generation_df = pd.read_excel(os.path.join(DATA_DIR, 'PVL_GenProfile.xlsx'), header = 0) # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n",
    "generation = generation_df['Generation (cm-3s-1)']\n",
    "depth = generation_df['Depth (um)']\n",
    "\n",
    "d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# starting defect state percentages\n",
    "nA_0 = 100\n",
    "nB_0 = 0\n",
    "nC_0 = 0\n",
    "\n",
    "mechanism_params = utilities.get_kinetics('repins')\n",
    "\n",
    "timesteps[['NA', 'NB', 'NC', 'tau']] = np.nan # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n",
    "\n",
    "timesteps.loc[0, ['NA', 'NB', 'NC']] = nA_0, nB_0, nC_0 # assign first timestep defect state percentages\n",
    "timesteps.loc[0, 'tau'] = letid.tau_now(tau_0, tau_deg, nB_0) # calculate tau for the first timestep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Injection</th>\n",
       "      <th>NA</th>\n",
       "      <th>NB</th>\n",
       "      <th>NC</th>\n",
       "      <th>tau</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999-01-01 00:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1999-01-01 01:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999-01-01 02:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999-01-01 03:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1999-01-01 04:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8755</th>\n",
       "      <td>1999-12-31 19:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>1999-12-31 20:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8757</th>\n",
       "      <td>1999-12-31 21:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8758</th>\n",
       "      <td>1999-12-31 22:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8759</th>\n",
       "      <td>1999-12-31 23:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\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 × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Datetime  Temperature  Injection     NA   NB   NC    tau\n",
       "0    1999-01-01 00:30:00-07:00          0.0        NaN  100.0  0.0  0.0  120.0\n",
       "1    1999-01-01 01:30:00-07:00          0.0        NaN    NaN  NaN  NaN    NaN\n",
       "2    1999-01-01 02:30:00-07:00          0.0        NaN    NaN  NaN  NaN    NaN\n",
       "3    1999-01-01 03:30:00-07:00          0.0        NaN    NaN  NaN  NaN    NaN\n",
       "4    1999-01-01 04:30:00-07:00          0.0        NaN    NaN  NaN  NaN    NaN\n",
       "...                        ...          ...        ...    ...  ...  ...    ...\n",
       "8755 1999-12-31 19:30:00-07:00          0.0        NaN    NaN  NaN  NaN    NaN\n",
       "8756 1999-12-31 20:30:00-07:00          0.0        NaN    NaN  NaN  NaN    NaN\n",
       "8757 1999-12-31 21:30:00-07:00          0.0        NaN    NaN  NaN  NaN    NaN\n",
       "8758 1999-12-31 22:30:00-07:00          0.0        NaN    NaN  NaN  NaN    NaN\n",
       "8759 1999-12-31 23:30:00-07:00          0.0        NaN    NaN  NaN  NaN    NaN\n",
       "\n",
       "[8760 rows x 7 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timesteps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run through timesteps\n",
    "Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, timestep in timesteps.iterrows():\n",
    "\n",
    "    # first row tau has already been assigned\n",
    "    if index == 0:\n",
    "        pass\n",
    "\n",
    "    # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n",
    "    else:\n",
    "        n_A = timesteps.at[index-1, 'NA']\n",
    "        n_B = timesteps.at[index-1, 'NB']\n",
    "        n_C = timesteps.at[index-1, 'NC']\n",
    "\n",
    "        tau = letid.tau_now(tau_0, tau_deg, n_B)\n",
    "        jsc = collection.calculate_jsc_from_tau_cp(tau, wafer_thickness, d_base, srv_rear, generation, depth)\n",
    "\n",
    "        temperature = timesteps.at[index, 'Temperature']\n",
    "        injection = timesteps.at[index, 'Injection']\n",
    "\n",
    "        # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n",
    "        k_AB = letid.k_ij(mechanism_params['v_ab'], mechanism_params['ea_ab'], temperature)\n",
    "        k_BA = letid.k_ij(mechanism_params['v_ba'], mechanism_params['ea_ba'], temperature)\n",
    "        k_BC = letid.k_ij(mechanism_params['v_bc'], mechanism_params['ea_bc'], temperature)\n",
    "        k_CB = letid.k_ij(mechanism_params['v_cb'], mechanism_params['ea_cb'], temperature)\n",
    "\n",
    "        x_ab = letid.carrier_factor(tau, 'ab', temperature, injection, jsc, wafer_thickness, srv_rear, mechanism_params)\n",
    "        x_ba = letid.carrier_factor(tau, 'ba', temperature, injection, jsc, wafer_thickness, srv_rear, mechanism_params)\n",
    "        x_bc = letid.carrier_factor(tau, 'bc', temperature, injection, jsc, wafer_thickness, srv_rear, mechanism_params)\n",
    "\n",
    "        # calculate the instantaneous change in NA, NB, and NC\n",
    "        dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n",
    "        dN_Bdt = (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n",
    "        dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n",
    "\n",
    "        t_step = (timesteps.at[index, 'Datetime'] - timesteps.at[index-1,'Datetime']).total_seconds()\n",
    "\n",
    "        # assign new defect state percentages\n",
    "        timesteps.at[index, 'NA'] = n_A + dN_Adt*t_step\n",
    "        timesteps.at[index, 'NB'] = n_B + dN_Bdt*t_step\n",
    "        timesteps.at[index, 'NC'] = n_C + dN_Cdt*t_step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "timesteps['tau'] = letid.tau_now(tau_0, tau_deg, timesteps['NB'])\n",
    "\n",
    "# calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc.\n",
    "for index, timestep in timesteps.iterrows():\n",
    "    jsc_now = collection.calculate_jsc_from_tau_cp(timesteps.at[index, 'tau'], wafer_thickness, d_base, srv_rear, generation, depth)\n",
    "    timesteps.at[index, 'Jsc'] = jsc_now\n",
    "    timesteps.at[index, 'Voc'] = letid.calc_voc_from_tau(timesteps.at[index, 'tau'], wafer_thickness, srv_rear, jsc_now, temperature = 25)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Datetime</th>\n",
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       "      <th>Jsc</th>\n",
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       "      <th>Isc</th>\n",
       "      <th>FF</th>\n",
       "      <th>Pmp</th>\n",
       "      <th>Pmp_norm</th>\n",
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       "  <tbody>\n",
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       "      <td>0.0</td>\n",
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       "      <td>6.732362e-15</td>\n",
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       "      <td>0.656761</td>\n",
       "      <td>9.955535</td>\n",
       "      <td>0.839287</td>\n",
       "      <td>5.487602</td>\n",
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       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>1999-12-31 20:30:00-07:00</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>41.343585</td>\n",
       "      <td>0.656761</td>\n",
       "      <td>9.955535</td>\n",
       "      <td>0.839287</td>\n",
       "      <td>5.487602</td>\n",
       "      <td>0.989573</td>\n",
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       "    <tr>\n",
       "      <th>8759</th>\n",
       "      <td>1999-12-31 23:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.470917</td>\n",
       "      <td>6.606591e+01</td>\n",
       "      <td>3.463173e+00</td>\n",
       "      <td>90.203213</td>\n",
       "      <td>41.343585</td>\n",
       "      <td>0.656761</td>\n",
       "      <td>9.955535</td>\n",
       "      <td>0.839287</td>\n",
       "      <td>5.487602</td>\n",
       "      <td>0.989573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Datetime  Temperature  Injection          NA  \\\n",
       "0    1999-01-01 00:30:00-07:00          0.0        NaN  100.000000   \n",
       "1    1999-01-01 01:30:00-07:00          0.0        NaN  100.000000   \n",
       "2    1999-01-01 02:30:00-07:00          0.0        NaN  100.000000   \n",
       "3    1999-01-01 03:30:00-07:00          0.0        NaN  100.000000   \n",
       "4    1999-01-01 04:30:00-07:00          0.0        NaN  100.000000   \n",
       "...                        ...          ...        ...         ...   \n",
       "8755 1999-12-31 19:30:00-07:00          0.0        NaN   30.470917   \n",
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       "8757 1999-12-31 21:30:00-07:00          0.0        NaN   30.470917   \n",
       "8758 1999-12-31 22:30:00-07:00          0.0        NaN   30.470917   \n",
       "8759 1999-12-31 23:30:00-07:00          0.0        NaN   30.470917   \n",
       "\n",
       "                NB            NC         tau        Jsc       Voc       Isc  \\\n",
       "0     0.000000e+00  0.000000e+00  120.000000  41.451328  0.661315  9.981480   \n",
       "1     1.683090e-15  0.000000e+00  120.000000  41.451328  0.661315  9.981480   \n",
       "2     3.366181e-15  5.269266e-36  120.000000  41.451328  0.661315  9.981480   \n",
       "3     5.049271e-15  1.580780e-35  120.000000  41.451328  0.661315  9.981480   \n",
       "4     6.732362e-15  3.161560e-35  120.000000  41.451328  0.661315  9.981480   \n",
       "...            ...           ...         ...        ...       ...       ...   \n",
       "8755  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
       "8756  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
       "8757  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
       "8758  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
       "8759  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
       "\n",
       "            FF       Pmp  Pmp_norm  \n",
       "0     0.840101  5.545424  1.000000  \n",
       "1     0.840101  5.545424  1.000000  \n",
       "2     0.840101  5.545424  1.000000  \n",
       "3     0.840101  5.545424  1.000000  \n",
       "4     0.840101  5.545424  1.000000  \n",
       "...        ...       ...       ...  \n",
       "8755  0.839287  5.487602  0.989573  \n",
       "8756  0.839287  5.487602  0.989573  \n",
       "8757  0.839287  5.487602  0.989573  \n",
       "8758  0.839287  5.487602  0.989573  \n",
       "8759  0.839287  5.487602  0.989573  \n",
       "\n",
       "[8760 rows x 13 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timesteps = letid.calc_device_params(timesteps, cell_area) # this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power\n",
    "\n",
    "timesteps\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note of course that all these calculated device parameters are modeled STC device parameters, not the instantaneous, weather-dependent values. We'll merge back in the pvlib results for convenience, but these don't reflect the device degradation. We'll calculate energy loss next"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
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       "      <th>...</th>\n",
       "      <th>p_mp</th>\n",
       "      <th>i_x</th>\n",
       "      <th>i_xx</th>\n",
       "      <th>Effective irradiance</th>\n",
       "      <th>ghi</th>\n",
       "      <th>dhi</th>\n",
       "      <th>dni</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>temp_air</th>\n",
       "      <th>precipitable_water</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999-01-01 00:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>41.451328</td>\n",
       "      <td>0.661315</td>\n",
       "      <td>9.981480</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.891869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1999-01-01 01:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.683090e-15</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>41.451328</td>\n",
       "      <td>0.661315</td>\n",
       "      <td>9.981480</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.908158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999-01-01 02:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3.366181e-15</td>\n",
       "      <td>5.269266e-36</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>41.451328</td>\n",
       "      <td>0.661315</td>\n",
       "      <td>9.981480</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.932199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999-01-01 03:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>5.049271e-15</td>\n",
       "      <td>1.580780e-35</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>41.451328</td>\n",
       "      <td>0.661315</td>\n",
       "      <td>9.981480</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.955004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1999-01-01 04:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>6.732362e-15</td>\n",
       "      <td>3.161560e-35</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>41.451328</td>\n",
       "      <td>0.661315</td>\n",
       "      <td>9.981480</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.963991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8755</th>\n",
       "      <td>1999-12-31 19:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.470917</td>\n",
       "      <td>6.606591e+01</td>\n",
       "      <td>3.463173e+00</td>\n",
       "      <td>90.203213</td>\n",
       "      <td>41.343585</td>\n",
       "      <td>0.656761</td>\n",
       "      <td>9.955535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.939501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>1999-12-31 20:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.470917</td>\n",
       "      <td>6.606591e+01</td>\n",
       "      <td>3.463173e+00</td>\n",
       "      <td>90.203213</td>\n",
       "      <td>41.343585</td>\n",
       "      <td>0.656761</td>\n",
       "      <td>9.955535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.975338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8757</th>\n",
       "      <td>1999-12-31 21:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.470917</td>\n",
       "      <td>6.606591e+01</td>\n",
       "      <td>3.463173e+00</td>\n",
       "      <td>90.203213</td>\n",
       "      <td>41.343585</td>\n",
       "      <td>0.656761</td>\n",
       "      <td>9.955535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.941186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8758</th>\n",
       "      <td>1999-12-31 22:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.470917</td>\n",
       "      <td>6.606591e+01</td>\n",
       "      <td>3.463173e+00</td>\n",
       "      <td>90.203213</td>\n",
       "      <td>41.343585</td>\n",
       "      <td>0.656761</td>\n",
       "      <td>9.955535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.912427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8759</th>\n",
       "      <td>1999-12-31 23:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.470917</td>\n",
       "      <td>6.606591e+01</td>\n",
       "      <td>3.463173e+00</td>\n",
       "      <td>90.203213</td>\n",
       "      <td>41.343585</td>\n",
       "      <td>0.656761</td>\n",
       "      <td>9.955535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.892318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Datetime  Temperature  Injection          NA  \\\n",
       "0    1999-01-01 00:30:00-07:00          0.0        NaN  100.000000   \n",
       "1    1999-01-01 01:30:00-07:00          0.0        NaN  100.000000   \n",
       "2    1999-01-01 02:30:00-07:00          0.0        NaN  100.000000   \n",
       "3    1999-01-01 03:30:00-07:00          0.0        NaN  100.000000   \n",
       "4    1999-01-01 04:30:00-07:00          0.0        NaN  100.000000   \n",
       "...                        ...          ...        ...         ...   \n",
       "8755 1999-12-31 19:30:00-07:00          0.0        NaN   30.470917   \n",
       "8756 1999-12-31 20:30:00-07:00          0.0        NaN   30.470917   \n",
       "8757 1999-12-31 21:30:00-07:00          0.0        NaN   30.470917   \n",
       "8758 1999-12-31 22:30:00-07:00          0.0        NaN   30.470917   \n",
       "8759 1999-12-31 23:30:00-07:00          0.0        NaN   30.470917   \n",
       "\n",
       "                NB            NC         tau        Jsc       Voc       Isc  \\\n",
       "0     0.000000e+00  0.000000e+00  120.000000  41.451328  0.661315  9.981480   \n",
       "1     1.683090e-15  0.000000e+00  120.000000  41.451328  0.661315  9.981480   \n",
       "2     3.366181e-15  5.269266e-36  120.000000  41.451328  0.661315  9.981480   \n",
       "3     5.049271e-15  1.580780e-35  120.000000  41.451328  0.661315  9.981480   \n",
       "4     6.732362e-15  3.161560e-35  120.000000  41.451328  0.661315  9.981480   \n",
       "...            ...           ...         ...        ...       ...       ...   \n",
       "8755  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
       "8756  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
       "8757  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
       "8758  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
       "8759  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
       "\n",
       "      ...  p_mp  i_x  i_xx  Effective irradiance  ghi  dhi  dni  wind_speed  \\\n",
       "0     ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         1.8   \n",
       "1     ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         1.7   \n",
       "2     ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         1.5   \n",
       "3     ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         1.3   \n",
       "4     ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         1.3   \n",
       "...   ...   ...  ...   ...                   ...  ...  ...  ...         ...   \n",
       "8755  ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         0.9   \n",
       "8756  ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         1.2   \n",
       "8757  ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         1.6   \n",
       "8758  ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         1.7   \n",
       "8759  ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         1.8   \n",
       "\n",
       "      temp_air  precipitable_water  \n",
       "0          0.0            0.891869  \n",
       "1          0.0            0.908158  \n",
       "2          0.0            0.932199  \n",
       "3          0.0            0.955004  \n",
       "4          0.0            0.963991  \n",
       "...        ...                 ...  \n",
       "8755       0.0            0.939501  \n",
       "8756       0.0            0.975338  \n",
       "8757       0.0            0.941186  \n",
       "8758       0.0            0.912427  \n",
       "8759       0.0            0.892318  \n",
       "\n",
       "[8760 rows x 27 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timesteps = timesteps.merge(mc.results.dc, left_on = 'Datetime', right_index=True)\n",
    "timesteps = timesteps.merge(pd.DataFrame(mc.results.effective_irradiance, columns= ['Effective irradiance']), left_on = 'Datetime', right_index=True)\n",
    "timesteps = timesteps.merge(mc.results.weather, left_on = 'Datetime', right_index=True)\n",
    "\n",
    "timesteps\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cycler import cycler\n",
    "plt.style.use('default')\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.set_prop_cycle(cycler('color', ['tab:blue', 'tab:orange', 'tab:green']) + cycler('linestyle', ['-', '--', '-.']))\n",
    "\n",
    "ax.plot(timesteps['Datetime'], timesteps[['NA', 'NB', 'NC']].values)\n",
    "ax.legend(labels = ['$N_A$', '$N_B$', '$N_C$'], loc = 'upper left')\n",
    "ax.set_ylabel('Defect state percentages [%]')\n",
    "ax.set_xlabel('Datetime')\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(timesteps['Datetime'], timesteps['Pmp_norm'], c = 'black', label = 'Normalized STC $P_{MP}$')\n",
    "ax2.legend(loc = 'upper right')\n",
    "ax2.set_ylabel('Normalized STC $P_{MP}$')\n",
    "\n",
    "ax.set_title('Simulated Outdoor LETID progression based on accelerated test results \\n'f'{location.name}')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "plt.style.use('default')\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(timesteps['Datetime'], timesteps['Pmp_norm'], c = 'black', label = 'Normalized STC $P_{MP}$')\n",
    "ax.plot(timesteps['Datetime'], np.ones(len(timesteps)), c = 'black')\n",
    "ax.fill_between(timesteps['Datetime'], np.ones(len(timesteps)), timesteps['Pmp_norm'], color = 'grey', alpha = 0.5)\n",
    "\n",
    "\n",
    "ax.set_ylabel('Normalized STC $P_{MP}$')\n",
    "\n",
    "ax.set_title('Energy Loss')\n",
    "\n",
    "loss = letid.calc_energy_loss(timesteps)\n",
    "\n",
    "ax.text(mdates.datestr2num('1999-08-02'), .994, s = f\"Energy loss = {loss*100:.2f}%\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_15280\\3402272254.py:45: DeprecationWarning: You are passing x=0          0.0\n",
      "1          1.0\n",
      "2          2.0\n",
      "3          3.0\n",
      "4          4.0\n",
      "         ...  \n",
      "8755    8755.0\n",
      "8756    8756.0\n",
      "8757    8757.0\n",
      "8758    8758.0\n",
      "8759    8759.0\n",
      "Name: Timedelta, Length: 8760, dtype: float64 as a positional argument. Please change your invocation to use keyword arguments. From SciPy 1.14, passing these as positional arguments will result in an error.\n",
      "  energy = simpson(timesteps[\"p_mp\"]/1000, timesteps['Timedelta'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "from scipy.integrate import cumulative_trapezoid, simpson\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(3, 1, figsize = (10,10), sharex = True)\n",
    "ax1, ax2, ax3 = axs\n",
    "\n",
    "ax1.plot(timesteps['Datetime'], timesteps[\"p_mp\"], label = 'Base $P_{MP}$')\n",
    "ax1.plot(timesteps['Datetime'], timesteps[\"p_mp\"]*timesteps['Pmp_norm'], label = 'Degraded $P_{MP}$', alpha = 0.7)\n",
    "ax1.legend(loc = 'lower left')\n",
    "\n",
    "axin1 = ax1.inset_axes([.55, 0.25, 0.43, 0.43])\n",
    "axin1.plot(timesteps['Datetime'], timesteps[\"p_mp\"])\n",
    "axin1.plot(timesteps['Datetime'], timesteps[\"p_mp\"]*timesteps['Pmp_norm'], alpha = 0.7)\n",
    "\n",
    "\n",
    "axin1.set_xlim(mdates.datestr2num('1999-12-04'), mdates.datestr2num('1999-12-11'))\n",
    "axin1.set_ylim(50,240)\n",
    "axin1.tick_params(axis='x', labelrotation = 15)\n",
    "ax1.indicate_inset_zoom(axin1)\n",
    "\n",
    "start = timesteps['Datetime'].iloc[0]\n",
    "timesteps['Timedelta'] = [(d - start).total_seconds()/3600 for d in timesteps['Datetime']]\n",
    "\n",
    "ax2.plot(timesteps['Datetime'], cumulative_trapezoid(timesteps[\"p_mp\"]/1000, timesteps['Timedelta'], initial = 0), label = 'Cumulative energy, base')\n",
    "ax2.plot(timesteps['Datetime'], cumulative_trapezoid((timesteps[\"p_mp\"]*timesteps['Pmp_norm'])/1000, timesteps['Timedelta'], initial = 0), label = 'Cumulative energy, degraded')\n",
    "ax2.fill_between(timesteps['Datetime'], (cumulative_trapezoid(timesteps[\"p_mp\"], timesteps['Timedelta'], initial = 0)/1000), (cumulative_trapezoid(timesteps[\"p_mp\"]*timesteps['Pmp_norm'], timesteps['Timedelta'], initial = 0)/1000), alpha = 0.5, color = 'C1', label = 'Energy loss')\n",
    "ax2.legend()\n",
    "\n",
    "axin2 = ax2.inset_axes([.55, 0.25, 0.43, 0.43])\n",
    "axin2.plot(timesteps['Datetime'], cumulative_trapezoid(timesteps[\"p_mp\"]/1000, timesteps['Timedelta'], initial = 0))\n",
    "axin2.plot(timesteps['Datetime'], cumulative_trapezoid((timesteps[\"p_mp\"]*timesteps['Pmp_norm'])/1000, timesteps['Timedelta'], initial = 0))\n",
    "axin2.fill_between(timesteps['Datetime'], (cumulative_trapezoid(timesteps[\"p_mp\"], timesteps['Timedelta'], initial = 0)/1000), (cumulative_trapezoid(timesteps[\"p_mp\"]*timesteps['Pmp_norm'], timesteps['Timedelta'], initial = 0)/1000), alpha = 0.5, color = 'C1')\n",
    "\n",
    "axin2.set_xlim(mdates.datestr2num('1999-12-04'), mdates.datestr2num('1999-12-11'))\n",
    "axin2.set_ylim(412, 427)\n",
    "axin2.tick_params(axis='x', labelrotation = 15)\n",
    "ax2.indicate_inset_zoom(axin2)\n",
    "\n",
    "ax3.set_xlabel('Datetime')\n",
    "ax3.plot(timesteps['Datetime'], (cumulative_trapezoid(timesteps[\"p_mp\"], timesteps['Timedelta'], initial = 0)/1000)-(cumulative_trapezoid(timesteps[\"p_mp\"]*timesteps['Pmp_norm'], timesteps['Timedelta'], initial = 0)/1000), label = 'Cumulative energy loss')\n",
    "ax3.legend()\n",
    "\n",
    "loss = letid.calc_energy_loss(timesteps)\n",
    "energy = simpson(timesteps[\"p_mp\"]/1000, timesteps['Timedelta'])\n",
    "ax3.text(mdates.datestr2num('1999-03-02'), 2, s = f\"Energy loss = {loss*energy:.1f} kWh ({loss*100:.2f}%)\")\n",
    "\n",
    "ax1.set_ylabel('Module $P_{MP}$ [W]')\n",
    "ax2.set_ylabel('Cumulative Energy [kWh]')\n",
    "ax3.set_ylabel('Cumulative Energy losses [kWh]')\n",
    "\n",
    "ax1.set_title('Module power, cumulative energy, and loss due to LETID')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The function `calc_letid_outdoors` wraps all of the steps above into a single function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                           Temperature  Injection          NA            NB  \\\n",
       "time                                                                          \n",
       "1999-01-01 00:30:00-07:00          0.0        NaN  100.000000  0.000000e+00   \n",
       "1999-01-01 01:30:00-07:00          0.0        NaN  100.000000  1.683090e-15   \n",
       "1999-01-01 02:30:00-07:00          0.0        NaN  100.000000  3.366181e-15   \n",
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       "1999-01-01 04:30:00-07:00          0.0        NaN  100.000000  6.732362e-15   \n",
       "...                                ...        ...         ...           ...   \n",
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       "1999-12-31 22:30:00-07:00          0.0        NaN   29.749662  6.666236e+01   \n",
       "1999-12-31 23:30:00-07:00          0.0        NaN   29.749662  6.666236e+01   \n",
       "\n",
       "                                     NC         tau        Jsc       Voc  \\\n",
       "time                                                                       \n",
       "1999-01-01 00:30:00-07:00  0.000000e+00  120.000000  41.451328  0.661315   \n",
       "1999-01-01 01:30:00-07:00  0.000000e+00  120.000000  41.451328  0.661315   \n",
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       "1999-01-01 03:30:00-07:00  1.580780e-35  120.000000  41.451328  0.661315   \n",
       "1999-01-01 04:30:00-07:00  3.161560e-35  120.000000  41.451328  0.661315   \n",
       "...                                 ...         ...        ...       ...   \n",
       "1999-12-31 19:30:00-07:00  3.587983e+00   90.001455  41.342623  0.656723   \n",
       "1999-12-31 20:30:00-07:00  3.587983e+00   90.001455  41.342623  0.656723   \n",
       "1999-12-31 21:30:00-07:00  3.587983e+00   90.001455  41.342623  0.656723   \n",
       "1999-12-31 22:30:00-07:00  3.587983e+00   90.001455  41.342623  0.656723   \n",
       "1999-12-31 23:30:00-07:00  3.587983e+00   90.001455  41.342623  0.656723   \n",
       "\n",
       "                                 Isc        FF       Pmp  Pmp_norm  \n",
       "time                                                                \n",
       "1999-01-01 00:30:00-07:00  10.072673  0.840101  5.596088  1.000000  \n",
       "1999-01-01 01:30:00-07:00  10.072673  0.840101  5.596088  1.000000  \n",
       "1999-01-01 02:30:00-07:00  10.072673  0.840101  5.596088  1.000000  \n",
       "1999-01-01 03:30:00-07:00  10.072673  0.840101  5.596088  1.000000  \n",
       "1999-01-01 04:30:00-07:00  10.072673  0.840101  5.596088  1.000000  \n",
       "...                              ...       ...       ...       ...  \n",
       "1999-12-31 19:30:00-07:00  10.046257  0.839281  5.537249  0.989486  \n",
       "1999-12-31 20:30:00-07:00  10.046257  0.839281  5.537249  0.989486  \n",
       "1999-12-31 21:30:00-07:00  10.046257  0.839281  5.537249  0.989486  \n",
       "1999-12-31 22:30:00-07:00  10.046257  0.839281  5.537249  0.989486  \n",
       "1999-12-31 23:30:00-07:00  10.046257  0.839281  5.537249  0.989486  \n",
       "\n",
       "[8760 rows x 12 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mechanism_params = 'repins'\n",
    "\n",
    "letid.calc_letid_outdoors(tau_0, tau_deg, wafer_thickness, srv_rear, nA_0, nB_0, nC_0, weather, meta, mechanism_params, generation_df, module_parameters = cec_module)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3.366181e-15</td>\n",
       "      <td>5.269266e-36</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>41.451328</td>\n",
       "      <td>0.661315</td>\n",
       "      <td>9.981480</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.932199</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999-01-01 03:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>5.049271e-15</td>\n",
       "      <td>1.580780e-35</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>41.451328</td>\n",
       "      <td>0.661315</td>\n",
       "      <td>9.981480</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.955004</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1999-01-01 04:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>6.732362e-15</td>\n",
       "      <td>3.161560e-35</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>41.451328</td>\n",
       "      <td>0.661315</td>\n",
       "      <td>9.981480</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.963991</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8755</th>\n",
       "      <td>1999-12-31 19:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.470917</td>\n",
       "      <td>6.606591e+01</td>\n",
       "      <td>3.463173e+00</td>\n",
       "      <td>90.203213</td>\n",
       "      <td>41.343585</td>\n",
       "      <td>0.656761</td>\n",
       "      <td>9.955535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.939501</td>\n",
       "      <td>8755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>1999-12-31 20:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.470917</td>\n",
       "      <td>6.606591e+01</td>\n",
       "      <td>3.463173e+00</td>\n",
       "      <td>90.203213</td>\n",
       "      <td>41.343585</td>\n",
       "      <td>0.656761</td>\n",
       "      <td>9.955535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.975338</td>\n",
       "      <td>8756.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8757</th>\n",
       "      <td>1999-12-31 21:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.470917</td>\n",
       "      <td>6.606591e+01</td>\n",
       "      <td>3.463173e+00</td>\n",
       "      <td>90.203213</td>\n",
       "      <td>41.343585</td>\n",
       "      <td>0.656761</td>\n",
       "      <td>9.955535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.941186</td>\n",
       "      <td>8757.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8758</th>\n",
       "      <td>1999-12-31 22:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.470917</td>\n",
       "      <td>6.606591e+01</td>\n",
       "      <td>3.463173e+00</td>\n",
       "      <td>90.203213</td>\n",
       "      <td>41.343585</td>\n",
       "      <td>0.656761</td>\n",
       "      <td>9.955535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.912427</td>\n",
       "      <td>8758.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8759</th>\n",
       "      <td>1999-12-31 23:30:00-07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.470917</td>\n",
       "      <td>6.606591e+01</td>\n",
       "      <td>3.463173e+00</td>\n",
       "      <td>90.203213</td>\n",
       "      <td>41.343585</td>\n",
       "      <td>0.656761</td>\n",
       "      <td>9.955535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.892318</td>\n",
       "      <td>8759.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Datetime  Temperature  Injection          NA  \\\n",
       "0    1999-01-01 00:30:00-07:00          0.0        NaN  100.000000   \n",
       "1    1999-01-01 01:30:00-07:00          0.0        NaN  100.000000   \n",
       "2    1999-01-01 02:30:00-07:00          0.0        NaN  100.000000   \n",
       "3    1999-01-01 03:30:00-07:00          0.0        NaN  100.000000   \n",
       "4    1999-01-01 04:30:00-07:00          0.0        NaN  100.000000   \n",
       "...                        ...          ...        ...         ...   \n",
       "8755 1999-12-31 19:30:00-07:00          0.0        NaN   30.470917   \n",
       "8756 1999-12-31 20:30:00-07:00          0.0        NaN   30.470917   \n",
       "8757 1999-12-31 21:30:00-07:00          0.0        NaN   30.470917   \n",
       "8758 1999-12-31 22:30:00-07:00          0.0        NaN   30.470917   \n",
       "8759 1999-12-31 23:30:00-07:00          0.0        NaN   30.470917   \n",
       "\n",
       "                NB            NC         tau        Jsc       Voc       Isc  \\\n",
       "0     0.000000e+00  0.000000e+00  120.000000  41.451328  0.661315  9.981480   \n",
       "1     1.683090e-15  0.000000e+00  120.000000  41.451328  0.661315  9.981480   \n",
       "2     3.366181e-15  5.269266e-36  120.000000  41.451328  0.661315  9.981480   \n",
       "3     5.049271e-15  1.580780e-35  120.000000  41.451328  0.661315  9.981480   \n",
       "4     6.732362e-15  3.161560e-35  120.000000  41.451328  0.661315  9.981480   \n",
       "...            ...           ...         ...        ...       ...       ...   \n",
       "8755  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
       "8756  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
       "8757  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
       "8758  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
       "8759  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
       "\n",
       "      ...  i_x  i_xx  Effective irradiance  ghi  dhi  dni  wind_speed  \\\n",
       "0     ...  0.0   0.0                   NaN  0.0  0.0  0.0         1.8   \n",
       "1     ...  0.0   0.0                   NaN  0.0  0.0  0.0         1.7   \n",
       "2     ...  0.0   0.0                   NaN  0.0  0.0  0.0         1.5   \n",
       "3     ...  0.0   0.0                   NaN  0.0  0.0  0.0         1.3   \n",
       "4     ...  0.0   0.0                   NaN  0.0  0.0  0.0         1.3   \n",
       "...   ...  ...   ...                   ...  ...  ...  ...         ...   \n",
       "8755  ...  0.0   0.0                   NaN  0.0  0.0  0.0         0.9   \n",
       "8756  ...  0.0   0.0                   NaN  0.0  0.0  0.0         1.2   \n",
       "8757  ...  0.0   0.0                   NaN  0.0  0.0  0.0         1.6   \n",
       "8758  ...  0.0   0.0                   NaN  0.0  0.0  0.0         1.7   \n",
       "8759  ...  0.0   0.0                   NaN  0.0  0.0  0.0         1.8   \n",
       "\n",
       "      temp_air  precipitable_water  Timedelta  \n",
       "0          0.0            0.891869        0.0  \n",
       "1          0.0            0.908158        1.0  \n",
       "2          0.0            0.932199        2.0  \n",
       "3          0.0            0.955004        3.0  \n",
       "4          0.0            0.963991        4.0  \n",
       "...        ...                 ...        ...  \n",
       "8755       0.0            0.939501     8755.0  \n",
       "8756       0.0            0.975338     8756.0  \n",
       "8757       0.0            0.941186     8757.0  \n",
       "8758       0.0            0.912427     8758.0  \n",
       "8759       0.0            0.892318     8759.0  \n",
       "\n",
       "[8760 rows x 28 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timesteps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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