examples/sythentic_example_fit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e31b523d-668f-4b18-9a37-f4eed50db9b7",
   "metadata": {},
   "source": [
    "# Example PVPRO run with synthetic data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ebb3042-e3d6-4f1e-9c4b-2e453f053976",
   "metadata": {},
   "source": [
    "This notebook generates a PV dataset from an NSRDB weather data file. The dataset is generated by specifying rates of change of single diode model parameters, so the ground truth is known. The simulation adds noise to the measured module temperature and plane-of-array irradiance in order to make a more realistic dataset.\n",
    "\n",
    "Using the sythetic data, the PVPRO method is used to extract single diode model parameters vs. time. The results are compared to the ground truth."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "216c80c5-bd12-4c2b-975b-103330b580e1",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8df8c2e0-5fa0-40ef-9e64-1982140e3605",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact, interact_manual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "864bcad8-f967-4918-83b0-f3429d658e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys\n",
    "import os\n",
    "\n",
    "# ADD pvpro module to path.\n",
    "module_path = os.path.abspath(os.path.join('..'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pvpro import PvProHandler\n",
    "from pvpro.preprocess import Preprocessor\n",
    "from pvpro.postprocess import analyze_yoy\n",
    "from pvpro.plotting import plot_results_timeseries\n",
    "\n",
    "from pvpro.singlediode import calculate_temperature_coeffs, pvlib_single_diode\n",
    "from pvlib.temperature import sapm_module, sapm_cell"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e96f858e-2aa7-4a31-ab96-9e3cabfaae8e",
   "metadata": {},
   "source": [
    "## Generate Synthetic Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47b7becf-597f-4a13-8c42-6e309ccd6e90",
   "metadata": {},
   "source": [
    "Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08726911-8209-49f9-a7c9-42ca835eb73e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import inspect\n",
    "import pvpro\n",
    "pvpro_abspath = os.path.dirname(os.path.abspath(inspect.getfile(pvpro)))\n",
    "filename =  os.path.join(pvpro_abspath,'data','NSRDB_data_compressed_123796_37.89_-122.26.npz')\n",
    "data = np.load(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1bc7926e-d077-434a-977c-23adfa4f911a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'dni': data['dni'],\n",
    "                   'ghi': data['ghi'],\n",
    "                   'temperature_air': data['temp_air'],\n",
    "                   'wind_speed': data['wind_speed'],\n",
    "                   'year': data['year'],\n",
    "                   'month': data['month'],\n",
    "                   'day': data['day'],\n",
    "                   'hour': data['hour'],\n",
    "                   'minute': data['minute']\n",
    "                   })\n",
    "\n",
    "df.index = pd.to_datetime(df[['year', 'month', 'day', 'hour', 'minute']])\n",
    "\n",
    "# # Clip shorter\n",
    "df = df[:'2002-01-01']\n",
    "\n",
    "# 15 minute interpolation\n",
    "df = df.resample('15T').interpolate('linear')\n",
    "\n",
    "# drop times when GHI is <= 1\n",
    "df.drop(df.index[df['ghi'] <= 1.], inplace=True)\n",
    "\n",
    "# assume poa = ghi, e.g., horizontal module\n",
    "df['poa_actual'] = df['ghi']\n",
    "\n",
    "# Calculate true module/cell temperature\n",
    "df['temperature_module_actual'] = sapm_module(\n",
    "    poa_global=df['poa_actual'],\n",
    "    temp_air=df['temperature_air'],\n",
    "    wind_speed=df['wind_speed'],\n",
    "    a=-3.56,\n",
    "    b=-0.075\n",
    ")\n",
    "\n",
    "df['temperature_cell_actual'] = sapm_cell(\n",
    "    poa_global=df['poa_actual'],\n",
    "    temp_air=df['temperature_air'],\n",
    "    wind_speed=df['wind_speed'],\n",
    "    a=-3.56,\n",
    "    b=-0.075,\n",
    "    deltaT=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cdccf38-a645-4631-a2d5-5a67ad17e8d0",
   "metadata": {},
   "source": [
    "Simulate some noise on the \"measured\" poa irradiance by multiplying Gaussian noise with the \"actual\" poa irradiance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a1ee9cc8-b03f-4f33-bc04-c2ecb38b1854",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fractional POA noise standard deviation. \n",
    "poa_noise_level = 0.02\n",
    "\n",
    "# Seed random generator to get same results each time.\n",
    "np.random.seed(0)\n",
    "df['poa_meas'] = df['poa_actual'] * np.random.normal(1, poa_noise_level, len(df))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3280dcc3-436a-4b9c-a83d-f21d512fed7c",
   "metadata": {},
   "source": [
    "The \"measured\" module temperature is created by adding normally distributed noise to the \"actual\" module temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1102adeb-dc84-4e3b-8ad1-626529977b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Temperature noise standard deviation in C.\n",
    "temperature_noise_level = 1.0\n",
    "\n",
    "df['temperature_module_meas'] = df['temperature_module_actual'] + \\\n",
    "        np.random.normal(0,temperature_noise_level, len(df))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "96f1d957-a9a8-447d-bce7-a5b827880391",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def step_change(start_val, end_val, t_years, t_step=2):\n",
    "    \"\"\"\n",
    "    Create a smooth step change time series. \n",
    "    \"\"\"\n",
    "    y = np.zeros_like(t_years) + start_val\n",
    "    y = y + (end_val - start_val) * (\n",
    "            np.arctan(10 * (t_years - 2)) / np.pi + 0.5)\n",
    "    return y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e233aa83-740f-4d05-a1b3-96929427620a",
   "metadata": {},
   "outputs": [],
   "source": [
    "q = 1.60218e-19  # Elementary charge in units of coulombs\n",
    "kb = 1.38066e-23  # Boltzmann's constant in units of J/K\n",
    "\n",
    "# time vector in years\n",
    "t_years = (df.index - df.index[0]).days / 365"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5989c860-7cd2-4549-8bd4-ed1d1018d6fd",
   "metadata": {},
   "source": [
    "Set the parameters and how they change with time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41e0907f-fc7d-4e94-9073-86b46572658b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cells_in_series'] = 60\n",
    "df['alpha_sc'] = 0.001\n",
    "df['diode_factor'] = 1.2 - 0.0 * t_years\n",
    "df['nNsVth_ref'] = df['diode_factor'] * df['cells_in_series'] * kb / q * (\n",
    "        273.15 + 25)\n",
    "df['photocurrent_ref'] = 6.0 - (0.1 * t_years - 0.05 * np.sin(2 * np.pi * t_years))\n",
    "df['saturation_current_ref'] = 1e-10 + 0.5e-9 * t_years\n",
    "# df['resistance_shunt_ref'] = step_change(1000, 100, t_years, 2)\n",
    "df['resistance_shunt_ref'] = 1e12\n",
    "df['conductance_shunt_extra'] = 0.000 + 0.0003 * t_years\n",
    "df['resistance_series_ref'] = step_change(0.35, 0.7, t_years)\n",
    "# df['resistance_series_ref'] = 0.35\n",
    "df['band_gap_ref'] = 1.121\n",
    "df['dEgdT'] = -0.0002677"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ff388200-40d7-4e0d-9e44-eaa4d0e0857b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done!\n"
     ]
    }
   ],
   "source": [
    "# Calculate module operation over time.\n",
    "out = pvlib_single_diode(\n",
    "    effective_irradiance=df['poa_actual'],\n",
    "    temperature_cell=df['temperature_cell_actual'],\n",
    "    resistance_shunt_ref=df['resistance_shunt_ref'],\n",
    "    resistance_series_ref=df['resistance_series_ref'],\n",
    "    diode_factor=df['diode_factor'],\n",
    "    cells_in_series=df['cells_in_series'],\n",
    "    alpha_isc=df['alpha_sc'],\n",
    "    photocurrent_ref=df['photocurrent_ref'],\n",
    "    saturation_current_ref=df['saturation_current_ref'],\n",
    "    Eg_ref=df['band_gap_ref'],\n",
    "    dEgdT=df['dEgdT'],\n",
    "    conductance_shunt_extra=df['conductance_shunt_extra'],\n",
    "    singlediode_method='newton',\n",
    "    ivcurve_pnts=None,\n",
    ")\n",
    "\n",
    "# Add to the dataframe\n",
    "for k in out.keys():\n",
    "    df[k] = out[k]\n",
    "\n",
    "# Calculate module reference conditions over time.\n",
    "out_ref = pvlib_single_diode(\n",
    "    effective_irradiance=1000,\n",
    "    temperature_cell=25,\n",
    "    resistance_shunt_ref=df['resistance_shunt_ref'],\n",
    "    resistance_series_ref=df['resistance_series_ref'],\n",
    "    diode_factor=df['diode_factor'],\n",
    "    cells_in_series=df['cells_in_series'],\n",
    "    alpha_isc=df['alpha_sc'],\n",
    "    photocurrent_ref=df['photocurrent_ref'],\n",
    "    saturation_current_ref=df['saturation_current_ref'],\n",
    "    Eg_ref=df['band_gap_ref'],\n",
    "    dEgdT=df['dEgdT'],\n",
    "    conductance_shunt_extra=df['conductance_shunt_extra'],\n",
    "    singlediode_method='newton',\n",
    "    ivcurve_pnts=None,\n",
    ")\n",
    "\n",
    "for k in out_ref.keys():\n",
    "    df[k + '_ref'] = out_ref[k]\n",
    "\n",
    "# Set DC operation point at MPP\n",
    "df['v_dc'] = df['v_mp']\n",
    "df['i_dc'] = df['i_mp']\n",
    "\n",
    "# Change DC operation point to Voc during low irradiance conditions\n",
    "df.loc[df['poa_actual'] < 50, 'v_dc'] = df.loc[\n",
    "    df['poa_actual'] < 50, 'v_oc']\n",
    "df.loc[df['poa_actual'] < 50, 'i_dc'] = 0\n",
    "\n",
    "\n",
    "df.loc[df.index.day==1, 'v_dc'] = df.loc[df.index.day==1, 'v_oc']\n",
    "df.loc[df.index.day==1, 'i_dc'] = 0\n",
    "\n",
    "\n",
    "# Save data\n",
    "# df.to_pickle('synth01_out.pkl')\n",
    "print('done!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2473f02c-dee4-4456-a89e-2b6ad6e40bc8",
   "metadata": {},
   "source": [
    "## Run Preprocess."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23699acc-dd82-48f1-a0fe-be7af35855b4",
   "metadata": {},
   "source": [
    "Load preprocessor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "23d0fd8c-69e1-4f5b-9098-f4c5afd8a808",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre = Preprocessor(df,\n",
    "                   voltage_dc_key='v_dc',\n",
    "                   current_dc_key='i_dc',\n",
    "                   temperature_module_key='temperature_module_meas',\n",
    "                   irradiance_poa_key='poa_meas',\n",
    "                   modules_per_string=1,\n",
    "                   parallel_strings=1,\n",
    "                   )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44d40f87-1f01-4cde-a8d2-c0fa5b50bbc4",
   "metadata": {},
   "source": [
    "Calculate cell temperature from moduule temperature and POA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e9c129c9-a276-4a2a-93ff-9b7fc2ca92ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cell temperature assigned to 'temperature_cell'\n"
     ]
    }
   ],
   "source": [
    "pre.calculate_cell_temperature(delta_T=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61643ebd-fdd3-47b2-9f70-8cdd55d97d9c",
   "metadata": {},
   "source": [
    "Two preprocessing modes, 'fast' and 'sdt'. Since this is clean synthetic data, we will use the 'fast' pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a5f59443-f094-4679-be7b-e5ed661ce576",
   "metadata": {},
   "outputs": [],
   "source": [
    "method='fast'\n",
    "if method=='sdt':\n",
    "    pre.run_preprocess_sdt(correct_dst=True)\n",
    "    pre.classify_points_sdt()\n",
    "    pre.build_operating_cls()\n",
    "\n",
    "elif method=='fast':\n",
    "    pre.classify_points_pva()\n",
    "    pre.build_operating_cls()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77aa1238-ab32-451d-aed4-4510ba136550",
   "metadata": {},
   "source": [
    "Make PvProHandler object to store data and run analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1e22b2b4-d0d5-4f8a-ad92-7b2952e22041",
   "metadata": {},
   "outputs": [],
   "source": [
    "pvp = PvProHandler(pre.df,\n",
    "                   system_name='synthetic',\n",
    "                   cells_in_series=60,\n",
    "                   resistance_shunt_ref=df['resistance_shunt_ref'].mean(),\n",
    "                   alpha_isc=0.001,\n",
    "                   voltage_key='v_dc',\n",
    "                   current_key='i_dc',\n",
    "                   temperature_cell_key='temperature_cell',\n",
    "                   irradiance_poa_key='poa_meas',\n",
    "                   modules_per_string=1,\n",
    "                   parallel_strings=1,\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d5056b22-9505-457e-969f-10907a6929ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dni</th>\n",
       "      <th>ghi</th>\n",
       "      <th>temperature_air</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>poa_actual</th>\n",
       "      <th>...</th>\n",
       "      <th>seq_index</th>\n",
       "      <th>temperature_cell</th>\n",
       "      <th>power_dc</th>\n",
       "      <th>clipped_times</th>\n",
       "      <th>high_v</th>\n",
       "      <th>missing_data</th>\n",
       "      <th>no_errors</th>\n",
       "      <th>low_p</th>\n",
       "      <th>daytime</th>\n",
       "      <th>operating_cls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1998-01-01 07:45:00</th>\n",
       "      <td>88.5</td>\n",
       "      <td>23.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>2.300781</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>9.882439</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01 08:00:00</th>\n",
       "      <td>177.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.300781</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>12.436980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01 08:15:00</th>\n",
       "      <td>238.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.351562</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>11.650855</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01 08:30:00</th>\n",
       "      <td>299.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.400391</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>13.231624</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01 08:45:00</th>\n",
       "      <td>165.5</td>\n",
       "      <td>104.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>12.784768</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</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>2002-01-01 15:45:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>60.5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>60.5</td>\n",
       "      <td>...</td>\n",
       "      <td>70368</td>\n",
       "      <td>14.221421</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 16:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.800781</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>...</td>\n",
       "      <td>70369</td>\n",
       "      <td>12.699330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 16:15:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.800781</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>...</td>\n",
       "      <td>70370</td>\n",
       "      <td>14.008826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 16:30:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.800781</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>70371</td>\n",
       "      <td>12.666300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 16:45:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.851562</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>...</td>\n",
       "      <td>70372</td>\n",
       "      <td>12.995997</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70373 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       dni    ghi  temperature_air  wind_speed    year  month  \\\n",
       "1998-01-01 07:45:00   88.5   23.5              9.5    2.300781  1998.0    1.0   \n",
       "1998-01-01 08:00:00  177.0   47.0             10.0    2.300781  1998.0    1.0   \n",
       "1998-01-01 08:15:00  238.0   77.0             10.0    2.351562  1998.0    1.0   \n",
       "1998-01-01 08:30:00  299.0  107.0             10.0    2.400391  1998.0    1.0   \n",
       "1998-01-01 08:45:00  165.5  104.0             10.5    2.500000  1998.0    1.0   \n",
       "...                    ...    ...              ...         ...     ...    ...   \n",
       "2002-01-01 15:45:00    0.0   60.5             13.0    2.750000  2002.0    1.0   \n",
       "2002-01-01 16:00:00    0.0   44.0             13.0    2.800781  2002.0    1.0   \n",
       "2002-01-01 16:15:00    0.0   30.5             13.0    2.800781  2002.0    1.0   \n",
       "2002-01-01 16:30:00    0.0   17.0             13.0    2.800781  2002.0    1.0   \n",
       "2002-01-01 16:45:00    0.0    8.5             13.0    2.851562  2002.0    1.0   \n",
       "\n",
       "                     day  hour  minute  poa_actual  ...  seq_index  \\\n",
       "1998-01-01 07:45:00  1.0   7.5    15.0        23.5  ...          0   \n",
       "1998-01-01 08:00:00  1.0   8.0     0.0        47.0  ...          1   \n",
       "1998-01-01 08:15:00  1.0   8.0    15.0        77.0  ...          2   \n",
       "1998-01-01 08:30:00  1.0   8.0    30.0       107.0  ...          3   \n",
       "1998-01-01 08:45:00  1.0   8.5    15.0       104.0  ...          4   \n",
       "...                  ...   ...     ...         ...  ...        ...   \n",
       "2002-01-01 15:45:00  1.0  15.5    15.0        60.5  ...      70368   \n",
       "2002-01-01 16:00:00  1.0  16.0     0.0        44.0  ...      70369   \n",
       "2002-01-01 16:15:00  1.0  16.0    15.0        30.5  ...      70370   \n",
       "2002-01-01 16:30:00  1.0  16.0    30.0        17.0  ...      70371   \n",
       "2002-01-01 16:45:00  1.0  16.5    15.0         8.5  ...      70372   \n",
       "\n",
       "                     temperature_cell  power_dc  clipped_times  high_v  \\\n",
       "1998-01-01 07:45:00          9.882439       0.0          False    True   \n",
       "1998-01-01 08:00:00         12.436980       0.0          False    True   \n",
       "1998-01-01 08:15:00         11.650855       0.0          False    True   \n",
       "1998-01-01 08:30:00         13.231624       0.0          False    True   \n",
       "1998-01-01 08:45:00         12.784768       0.0          False    True   \n",
       "...                               ...       ...            ...     ...   \n",
       "2002-01-01 15:45:00         14.221421       0.0          False    True   \n",
       "2002-01-01 16:00:00         12.699330       0.0          False    True   \n",
       "2002-01-01 16:15:00         14.008826       0.0          False    True   \n",
       "2002-01-01 16:30:00         12.666300       0.0          False    True   \n",
       "2002-01-01 16:45:00         12.995997       0.0          False    True   \n",
       "\n",
       "                     missing_data  no_errors  low_p  daytime  operating_cls  \n",
       "1998-01-01 07:45:00         False       True   True    False              1  \n",
       "1998-01-01 08:00:00         False       True   True    False              1  \n",
       "1998-01-01 08:15:00         False       True   True    False              1  \n",
       "1998-01-01 08:30:00         False       True   True    False              1  \n",
       "1998-01-01 08:45:00         False       True   True    False              1  \n",
       "...                           ...        ...    ...      ...            ...  \n",
       "2002-01-01 15:45:00         False       True   True    False              1  \n",
       "2002-01-01 16:00:00         False       True   True    False              1  \n",
       "2002-01-01 16:15:00         False       True   True    False              1  \n",
       "2002-01-01 16:30:00         False       True   True    False              1  \n",
       "2002-01-01 16:45:00         False       True   True    False              1  \n",
       "\n",
       "[70373 rows x 51 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pvp.df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5adfeddf-f817-43dd-8ab6-25aeda628295",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated startpoint:\n",
      "{'diode_factor': 1.3455328800176904, 'photocurrent_ref': 5.962660540743577, 'saturation_current_ref': 7.473157878051913e-09, 'resistance_series_ref': 0.4298068719342463, 'resistance_shunt_ref': 400, 'conductance_shunt_extra': 0.0, 'v_oc_ref': 42.5216537621072, 'i_mp_ref': 5.560600479054517, 'i_sc_ref': 5.934153507862071, 'v_mp_ref': 34.54610657876529, 'p_mp_ref': 192.09709679135068, 'alpha_isc': 0.002980375307397469, 'alpha_imp': -0.00745244223049655, 'beta_vmp': -0.21029326536680307, 'beta_voc': -0.2054198080862189, 'cells_in_series': 60, 'nNsVth_ref': 2.0749686798232805}\n"
     ]
    }
   ],
   "source": [
    "# Estimate startpoint.\n",
    "pvp.estimate_p0(\n",
    "    boolean_mask = np.logical_and.reduce(\n",
    "        (pvp.df['operating_cls']==0,\n",
    "        pvp.df[pvp.irradiance_poa_key]>300)\n",
    "    )\n",
    ")\n",
    "# pvp.p0['diode_factor'] = 1.1\n",
    "print('Estimated startpoint:')\n",
    "print(pvp.p0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "744a8b95-83d1-4eb5-a96b-94e5f751a513",
   "metadata": {},
   "source": [
    "Can set a custom startpoint if auto-chosen startpoint isn't great."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "208300d5-0ec1-477c-a403-e466913c013e",
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    pvp.p0 = {'diode_factor': 1.2,\n",
    "          'photocurrent_ref': 5.9,\n",
    "          'saturation_current_ref': 6e-9,\n",
    "          'resistance_series_ref': 0.4,\n",
    "          'conductance_shunt_extra': 0.001}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8524059f-c371-4cfc-b280-887d05507797",
   "metadata": {},
   "source": [
    "Plot startpoint on top of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "333448a1-4c79-4d98-8ce6-b60ca5cd2652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pvp.plot_Vmp_Imp_scatter(df=pvp.df[:5000],\n",
    "                         p_plot=pvp.p0,\n",
    "                         figure_number=4,\n",
    "                         plot_vmp_max=42,\n",
    "                         plot_imp_max=6)\n",
    "plt.title('Startpoint')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2ff93a1-53dc-4e2d-8894-08f3dbbd58fd",
   "metadata": {},
   "source": [
    "Set boolean mask for which points to include. For this clean data, we are choosing a very simple mask. For a real PV dataset, more filtering on bad data is typically required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5f1f15d2-d222-42a3-b39d-798490fc0a51",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "boolean_mask = pvp.df['poa_meas'] > 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d3dabf8-ca2f-4b84-8370-57c82cbc2271",
   "metadata": {},
   "source": [
    "Set hyperparameters for running model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "737bbf05-22ab-4ebc-9ad5-12cd796d4160",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set hyperparameters for running model.\n",
    "hyperparams = {\n",
    "    'use_voc_points': False,\n",
    "    'use_mpp_points': True,\n",
    "    'use_clip_points': False,\n",
    "    'method': 'minimize',\n",
    "    'solver': 'L-BFGS-B',\n",
    "    # 'solver': 'nelder-mead',\n",
    "    'diode_factor': pvp.p0['diode_factor'],\n",
    "    'days_per_run': 30,\n",
    "    'iterations_per_year': 12,\n",
    "    'save_figs': False,\n",
    "    'verbose' : False,\n",
    "    'saturation_current_multistart':[0.7,1.0,1.3],\n",
    "#     'saturation_current_multistart':[1.0],\n",
    "    'start_point_method': 'last',\n",
    "    'save_figs_directory': 'figures',\n",
    "    'plot_imp_max': 7,\n",
    "    'plot_vmp_max': 35,\n",
    "    'boolean_mask': boolean_mask,\n",
    "    'singlediode_method':'fast'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3b9f706d-b552-4d77-86ab-6370ea1dfddf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 48/48 [00:21<00:00,  2.28it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 0.35 min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "ret = pvp.execute(iteration='all',\n",
    "                  **hyperparams)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "654ea5e5-da4c-43ce-ba33-0ab2af67c713",
   "metadata": {},
   "source": [
    "Get results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "598348cf-53bb-46d4-9b17-b634a2a1d4ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "pfit = pvp.result['p']\n",
    "# Optional: throw away high residual \"bad fit\" points.\n",
    "# pfit = pfit[pfit['residual']< np.percentile(pfit['residual'],90)].copy()\n",
    "pfit.dropna(inplace=True)\n",
    "pfit.index = pfit['t_start']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "25ebe2ab-23cf-4587-b761-6e5ba5efa67a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t_start</th>\n",
       "      <th>t_end</th>\n",
       "      <th>diode_factor_est</th>\n",
       "      <th>photocurrent_ref_est</th>\n",
       "      <th>saturation_current_ref_est</th>\n",
       "      <th>resistance_series_ref_est</th>\n",
       "      <th>resistance_shunt_ref_est</th>\n",
       "      <th>conductance_shunt_extra_est</th>\n",
       "      <th>v_oc_ref_est</th>\n",
       "      <th>i_mp_ref_est</th>\n",
       "      <th>...</th>\n",
       "      <th>nNsVth_ref</th>\n",
       "      <th>i_sc_ref</th>\n",
       "      <th>v_oc_ref</th>\n",
       "      <th>i_mp_ref</th>\n",
       "      <th>v_mp_ref</th>\n",
       "      <th>p_mp_ref</th>\n",
       "      <th>i_x_ref</th>\n",
       "      <th>i_xx_ref</th>\n",
       "      <th>t_mean</th>\n",
       "      <th>t_years</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_start</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1998-01-01 07:45:00</th>\n",
       "      <td>1998-01-01 07:45:00</td>\n",
       "      <td>1998-01-31 07:45:00</td>\n",
       "      <td>1.016009</td>\n",
       "      <td>6.094698</td>\n",
       "      <td>1.382201e-10</td>\n",
       "      <td>0.647452</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.352863</td>\n",
       "      <td>5.683735</td>\n",
       "      <td>...</td>\n",
       "      <td>2.074969</td>\n",
       "      <td>6.040745</td>\n",
       "      <td>46.099609</td>\n",
       "      <td>5.710807</td>\n",
       "      <td>38.019974</td>\n",
       "      <td>217.124742</td>\n",
       "      <td>6.040488</td>\n",
       "      <td>4.245566</td>\n",
       "      <td>1998-01-16 07:45:00</td>\n",
       "      <td>1998.041068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-31 18:15:00</th>\n",
       "      <td>1998-01-31 18:15:00</td>\n",
       "      <td>1998-03-02 18:15:00</td>\n",
       "      <td>0.879358</td>\n",
       "      <td>6.113835</td>\n",
       "      <td>3.186043e-12</td>\n",
       "      <td>0.745509</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.016405</td>\n",
       "      <td>5.701581</td>\n",
       "      <td>...</td>\n",
       "      <td>2.074969</td>\n",
       "      <td>6.061406</td>\n",
       "      <td>45.452148</td>\n",
       "      <td>5.729619</td>\n",
       "      <td>37.699407</td>\n",
       "      <td>216.003246</td>\n",
       "      <td>6.061150</td>\n",
       "      <td>4.312119</td>\n",
       "      <td>1998-02-15 18:15:00</td>\n",
       "      <td>1998.123203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-03-03 04:45:00</th>\n",
       "      <td>1998-03-03 04:45:00</td>\n",
       "      <td>1998-04-02 04:45:00</td>\n",
       "      <td>0.961739</td>\n",
       "      <td>6.104817</td>\n",
       "      <td>4.180306e-11</td>\n",
       "      <td>0.665534</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.743390</td>\n",
       "      <td>5.693171</td>\n",
       "      <td>...</td>\n",
       "      <td>2.074969</td>\n",
       "      <td>6.053361</td>\n",
       "      <td>45.024414</td>\n",
       "      <td>5.722897</td>\n",
       "      <td>37.588180</td>\n",
       "      <td>215.113272</td>\n",
       "      <td>6.053121</td>\n",
       "      <td>4.364347</td>\n",
       "      <td>1998-03-18 04:45:00</td>\n",
       "      <td>1998.208077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-04-02 15:15:00</th>\n",
       "      <td>1998-04-02 15:15:00</td>\n",
       "      <td>1998-05-02 15:15:00</td>\n",
       "      <td>1.029411</td>\n",
       "      <td>6.102398</td>\n",
       "      <td>2.483583e-10</td>\n",
       "      <td>0.600744</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.566879</td>\n",
       "      <td>5.690915</td>\n",
       "      <td>...</td>\n",
       "      <td>2.074969</td>\n",
       "      <td>6.040806</td>\n",
       "      <td>44.636719</td>\n",
       "      <td>5.710839</td>\n",
       "      <td>37.301823</td>\n",
       "      <td>213.024719</td>\n",
       "      <td>6.040556</td>\n",
       "      <td>4.372632</td>\n",
       "      <td>1998-04-17 15:15:00</td>\n",
       "      <td>1998.290212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-05-03 01:45:00</th>\n",
       "      <td>1998-05-03 01:45:00</td>\n",
       "      <td>1998-06-02 01:45:00</td>\n",
       "      <td>0.997089</td>\n",
       "      <td>6.063851</td>\n",
       "      <td>8.238116e-11</td>\n",
       "      <td>0.634597</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.878353</td>\n",
       "      <td>5.654968</td>\n",
       "      <td>...</td>\n",
       "      <td>2.074969</td>\n",
       "      <td>6.023872</td>\n",
       "      <td>44.446289</td>\n",
       "      <td>5.690411</td>\n",
       "      <td>36.925222</td>\n",
       "      <td>210.119677</td>\n",
       "      <td>6.023581</td>\n",
       "      <td>4.312808</td>\n",
       "      <td>1998-05-18 01:45:00</td>\n",
       "      <td>1998.375086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                t_start               t_end  diode_factor_est  \\\n",
       "t_start                                                                         \n",
       "1998-01-01 07:45:00 1998-01-01 07:45:00 1998-01-31 07:45:00          1.016009   \n",
       "1998-01-31 18:15:00 1998-01-31 18:15:00 1998-03-02 18:15:00          0.879358   \n",
       "1998-03-03 04:45:00 1998-03-03 04:45:00 1998-04-02 04:45:00          0.961739   \n",
       "1998-04-02 15:15:00 1998-04-02 15:15:00 1998-05-02 15:15:00          1.029411   \n",
       "1998-05-03 01:45:00 1998-05-03 01:45:00 1998-06-02 01:45:00          0.997089   \n",
       "\n",
       "                     photocurrent_ref_est  saturation_current_ref_est  \\\n",
       "t_start                                                                 \n",
       "1998-01-01 07:45:00              6.094698                1.382201e-10   \n",
       "1998-01-31 18:15:00              6.113835                3.186043e-12   \n",
       "1998-03-03 04:45:00              6.104817                4.180306e-11   \n",
       "1998-04-02 15:15:00              6.102398                2.483583e-10   \n",
       "1998-05-03 01:45:00              6.063851                8.238116e-11   \n",
       "\n",
       "                     resistance_series_ref_est  resistance_shunt_ref_est  \\\n",
       "t_start                                                                    \n",
       "1998-01-01 07:45:00                   0.647452                     400.0   \n",
       "1998-01-31 18:15:00                   0.745509                     400.0   \n",
       "1998-03-03 04:45:00                   0.665534                     400.0   \n",
       "1998-04-02 15:15:00                   0.600744                     400.0   \n",
       "1998-05-03 01:45:00                   0.634597                     400.0   \n",
       "\n",
       "                     conductance_shunt_extra_est  v_oc_ref_est  i_mp_ref_est  \\\n",
       "t_start                                                                        \n",
       "1998-01-01 07:45:00                          0.0     47.352863      5.683735   \n",
       "1998-01-31 18:15:00                          0.0     46.016405      5.701581   \n",
       "1998-03-03 04:45:00                          0.0     45.743390      5.693171   \n",
       "1998-04-02 15:15:00                          0.0     45.566879      5.690915   \n",
       "1998-05-03 01:45:00                          0.0     44.878353      5.654968   \n",
       "\n",
       "                     ...  nNsVth_ref  i_sc_ref   v_oc_ref  i_mp_ref  \\\n",
       "t_start              ...                                              \n",
       "1998-01-01 07:45:00  ...    2.074969  6.040745  46.099609  5.710807   \n",
       "1998-01-31 18:15:00  ...    2.074969  6.061406  45.452148  5.729619   \n",
       "1998-03-03 04:45:00  ...    2.074969  6.053361  45.024414  5.722897   \n",
       "1998-04-02 15:15:00  ...    2.074969  6.040806  44.636719  5.710839   \n",
       "1998-05-03 01:45:00  ...    2.074969  6.023872  44.446289  5.690411   \n",
       "\n",
       "                      v_mp_ref    p_mp_ref   i_x_ref  i_xx_ref  \\\n",
       "t_start                                                          \n",
       "1998-01-01 07:45:00  38.019974  217.124742  6.040488  4.245566   \n",
       "1998-01-31 18:15:00  37.699407  216.003246  6.061150  4.312119   \n",
       "1998-03-03 04:45:00  37.588180  215.113272  6.053121  4.364347   \n",
       "1998-04-02 15:15:00  37.301823  213.024719  6.040556  4.372632   \n",
       "1998-05-03 01:45:00  36.925222  210.119677  6.023581  4.312808   \n",
       "\n",
       "                                 t_mean      t_years  \n",
       "t_start                                               \n",
       "1998-01-01 07:45:00 1998-01-16 07:45:00  1998.041068  \n",
       "1998-01-31 18:15:00 1998-02-15 18:15:00  1998.123203  \n",
       "1998-03-03 04:45:00 1998-03-18 04:45:00  1998.208077  \n",
       "1998-04-02 15:15:00 1998-04-17 15:15:00  1998.290212  \n",
       "1998-05-03 01:45:00 1998-05-18 01:45:00  1998.375086  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pfit.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4fb9e0c-7b6a-47ce-9184-bf8750cfee8f",
   "metadata": {},
   "source": [
    "Inspect fit result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "89524957-7b96-4c5b-8c9e-c2e20165d6ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "559000c17ae94df39412f1da0f6810a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=24, description='k', max=48), Checkbox(value=True, description='filter')…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact\n",
    "def inspect_results(k=(0,len(pfit),1),filter=True):\n",
    "\n",
    "    cax = np.logical_and(\n",
    "        pvp.df.index>pfit['t_start'][k],\n",
    "        pvp.df.index<pfit['t_end'][k]\n",
    "    )\n",
    "    if filter:\n",
    "        mask = boolean_mask[cax]\n",
    "    else:\n",
    "        mask = np.ones_like(boolean_mask[cax])\n",
    "    plt.figure(figsize=(12,4))\n",
    "    plt.subplot(1,2,1)\n",
    "    pvp.plot_Vmp_Imp_scatter(df=pvp.df[cax][mask],\n",
    "                             p_plot=pfit.iloc[k],\n",
    "                            plot_vmp_max=37,\n",
    "                            plot_imp_max=7)\n",
    "    plt.subplot(1,2,2)\n",
    "    pvp.plot_current_irradiance_mpp_scatter(df=pvp.df[cax][mask],\n",
    "\n",
    "                                            p_plot=pfit.iloc[k],\n",
    "                            plot_imp_max=7)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b2bcee6-ff9b-48a2-9fdb-982a22638f7e",
   "metadata": {},
   "source": [
    "Analyze year-on-year trend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "55f4caf1-af6e-4897-9df1-cae1ff20f876",
   "metadata": {},
   "outputs": [],
   "source": [
    "yoy_result = analyze_yoy(pfit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f4c7d83-b89d-4453-95a9-3a84f03d0e3c",
   "metadata": {},
   "source": [
    "Format ground truth data for comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4acb4f25-8f72-4ff7-8776-2b6a053a9dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "compare = pvp.df.resample('M').mean()\n",
    "compare['t_years'] = np.array(\n",
    "            [t.year + (t.dayofyear - 1) / 365.25 for t in compare.index])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "776c6200-abb3-49a9-a31d-c42bc0aeeeb2",
   "metadata": {},
   "source": [
    "Compare results in a plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e906a22b-c118-40d1-8c3c-f4150c124dce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6102438025415354\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 700x600 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extra_text = 'System: {}\\n'.format(pvp.system_name) + \\\n",
    "                 'Use mpp points: {}\\n'.format(hyperparams['use_mpp_points']) + \\\n",
    "                 'Use voc points: {}\\n'.format(hyperparams['use_voc_points']) + \\\n",
    "                 'Use clip points: {}\\n'.format(hyperparams['use_clip_points']) + \\\n",
    "                 'Temperature noise: {} C\\n'.format(temperature_noise_level) + \\\n",
    "                 'Irradiance noise: {:.1%} \\n'.format(poa_noise_level) + \\\n",
    "                 'Days per run: {}\\n'.format(hyperparams['days_per_run']) + \\\n",
    "                 'start point method: {}\\n'.format(hyperparams['start_point_method']) + \\\n",
    "                 'Median residual: {:.4f}\\n'.format(1000*np.median(pfit['residual']))\n",
    "\n",
    "\n",
    "\n",
    "# Plot results\n",
    "plot_results_timeseries(pfit,yoy_result=yoy_result,\n",
    "                        extra_text=extra_text,\n",
    "                        compare=compare)\n",
    "# plt.savefig('figures/synth02_result.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1635c8b-70d7-40b2-abf3-18aa6c935f0a",
   "metadata": {},
   "source": [
    "Form comparison using rms error from extracted to true. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "39795b93-304c-4abb-ba30-fc5be4d48031",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pvpro.postprocess import calculate_rms_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "db75bb92-f148-433d-bbc1-09093dbc3b93",
   "metadata": {},
   "outputs": [],
   "source": [
    "rms_error = calculate_rms_error(pfit,df,zero_mean=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c73d43d3-d6a6-4da3-96ac-76bc949db421",
   "metadata": {},
   "source": [
    "Inspect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "469b37d1-39cf-492a-8f12-2229089f1328",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diode_factor               1.199178e-16\n",
       "photocurrent_ref           1.903264e-02\n",
       "saturation_current_ref     3.898788e-09\n",
       "resistance_series_ref      1.315753e-01\n",
       "conductance_shunt_extra    1.364663e-04\n",
       "resistance_shunt_ref       0.000000e+00\n",
       "nNsVth_ref                 1.949488e-16\n",
       "i_sc_ref                   1.826793e-02\n",
       "v_oc_ref                   1.829607e-01\n",
       "i_mp_ref                   1.008096e-02\n",
       "v_mp_ref                   6.720123e-01\n",
       "p_mp_ref                   3.757021e+00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(rms_error)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}