Eddy Lite Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "432cb49a-7571-4bfa-aeaf-32085677a53d",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "376fa0df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T13:07:05.717447Z",
     "start_time": "2021-08-05T13:06:59.955222Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import requests\n",
    "import seaborn as sns\n",
    "\n",
    "import scipy\n",
    "import scipy.optimize as op\n",
    "from scipy.stats import linregress\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy import stats\n",
    "\n",
    "from datetime import date, timedelta\n",
    "import datetime\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n",
    "\n",
    "from matplotlib.backends.backend_pdf import PdfPages\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.covariance import EmpiricalCovariance, MinCovDet\n",
    "\n",
    "import dask.dataframe as dd\n",
    "\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 12, 10\n",
    "rcParams['pdf.fonttype'] = 42\n",
    "inline_rc = dict(plt.rcParams)\n",
    "\n",
    "#from matplotlib.pyplot import cm \n",
    "import platform\n",
    "import sys\n",
    "import glob\n",
    "import io\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f42aec5b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T13:07:06.641216Z",
     "start_time": "2021-08-05T13:07:05.751323Z"
    }
   },
   "outputs": [],
   "source": [
    "import ec"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "511290ff-b755-4d63-9c44-baf9ab5cba7b",
   "metadata": {},
   "source": [
    "# Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5fbb660-55c7-4f92-8ab3-613cec74407c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cf5db399",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-01T18:26:06.083929Z",
     "start_time": "2021-07-01T18:22:26.051436Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_1.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_2.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_3.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_4.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_5.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_6.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_7.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_8.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_9.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_10.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_11.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_12.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_13.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_14.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_15.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_16.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_17.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_18.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_19.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_20.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_21.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_22.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_23.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_24.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_25.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_26.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_27.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_28.dat\n",
      "G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Download_2019_03_12\\TOA5_8441_Time_Series_29.dat\n"
     ]
    }
   ],
   "source": [
    "dfs = {}\n",
    "i=0\n",
    "fileplace = \"G:/Shared drives/UGS_Groundwater/Projects/Juab_Valley/Climate/ET_EddyCovariance/Data_Downloads/Juab/combined_farm/\"\n",
    "for file in glob.glob(fileplace+\"*Time_Series*\"):\n",
    "    print(file)\n",
    "    df = dd.read_csv(file,skiprows=[0,2,3],na_values=['NAN'],dtype={'diag_csat': 'float64'},\n",
    "                 parse_dates=True).set_index('TIMESTAMP')\n",
    "    #df['Pr'] = 86000\n",
    "\n",
    "    df.index = df.index.astype('M8[ns]')\n",
    "    dfs[i] = df.compute()\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6f34e37b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T14:21:03.612859Z",
     "start_time": "2021-08-05T14:21:00.876423Z"
    }
   },
   "outputs": [],
   "source": [
    "import dask.dataframe as dd\n",
    "fileplace = \"C:/Users/paulinkenbrandt/Documents/Projects/Juab/20210622_farm_processed/\"\n",
    "file= fileplace + \"*Time_Series_290.dat\"\n",
    "df = dd.read_csv(file,skiprows=[0,2,3],na_values=['NAN'],dtype={'diag_csat': 'float64'},\n",
    "                 parse_dates=True).set_index('TIMESTAMP')\n",
    "#df['Pr'] = 86000\n",
    "\n",
    "df.index = df.index.astype('M8[ns]')\n",
    "df = df.compute()\n",
    "df['TA_1_1_1'] = df['TA_1_1_1'] + 23 \n",
    "df['T_SONIC'] = df['TA_1_1_1']\n",
    "#df = df.loc[pd.to_datetime(\"2009-05-27 16:00:00.000\"):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "de8b79fc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T14:21:07.916326Z",
     "start_time": "2021-08-05T14:21:07.892389Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RECORD</th>\n",
       "      <th>Ux</th>\n",
       "      <th>Uy</th>\n",
       "      <th>Uz</th>\n",
       "      <th>T_SONIC</th>\n",
       "      <th>diag_sonic</th>\n",
       "      <th>volt_KH20</th>\n",
       "      <th>diag_kh20</th>\n",
       "      <th>amb_press</th>\n",
       "      <th>TA_1_1_1</th>\n",
       "      <th>RH_1_1_1</th>\n",
       "      <th>FW</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMESTAMP</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-06-14 00:00:00.100</th>\n",
       "      <td>245827970</td>\n",
       "      <td>4.980349</td>\n",
       "      <td>-3.585071</td>\n",
       "      <td>-0.376115</td>\n",
       "      <td>31.569908</td>\n",
       "      <td>0</td>\n",
       "      <td>34.73232</td>\n",
       "      <td>0</td>\n",
       "      <td>49.94021</td>\n",
       "      <td>31.569908</td>\n",
       "      <td>12.12791</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-14 00:00:00.200</th>\n",
       "      <td>245827971</td>\n",
       "      <td>5.136191</td>\n",
       "      <td>-3.499528</td>\n",
       "      <td>-0.504765</td>\n",
       "      <td>31.566185</td>\n",
       "      <td>0</td>\n",
       "      <td>35.63925</td>\n",
       "      <td>0</td>\n",
       "      <td>49.94020</td>\n",
       "      <td>31.566185</td>\n",
       "      <td>12.12012</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-14 00:00:00.300</th>\n",
       "      <td>245827972</td>\n",
       "      <td>5.294898</td>\n",
       "      <td>-2.926585</td>\n",
       "      <td>-0.918012</td>\n",
       "      <td>31.544762</td>\n",
       "      <td>0</td>\n",
       "      <td>34.99607</td>\n",
       "      <td>0</td>\n",
       "      <td>49.93660</td>\n",
       "      <td>31.544762</td>\n",
       "      <td>12.12461</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-14 00:00:00.400</th>\n",
       "      <td>245827973</td>\n",
       "      <td>5.029508</td>\n",
       "      <td>-2.619484</td>\n",
       "      <td>-0.593327</td>\n",
       "      <td>31.560478</td>\n",
       "      <td>0</td>\n",
       "      <td>34.75459</td>\n",
       "      <td>0</td>\n",
       "      <td>49.92876</td>\n",
       "      <td>31.560478</td>\n",
       "      <td>12.12626</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-14 00:00:00.500</th>\n",
       "      <td>245827974</td>\n",
       "      <td>4.909807</td>\n",
       "      <td>-2.949019</td>\n",
       "      <td>-0.766270</td>\n",
       "      <td>31.576233</td>\n",
       "      <td>0</td>\n",
       "      <td>34.93779</td>\n",
       "      <td>0</td>\n",
       "      <td>49.92938</td>\n",
       "      <td>31.576233</td>\n",
       "      <td>12.12276</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            RECORD        Ux        Uy        Uz    T_SONIC  \\\n",
       "TIMESTAMP                                                                     \n",
       "2021-06-14 00:00:00.100  245827970  4.980349 -3.585071 -0.376115  31.569908   \n",
       "2021-06-14 00:00:00.200  245827971  5.136191 -3.499528 -0.504765  31.566185   \n",
       "2021-06-14 00:00:00.300  245827972  5.294898 -2.926585 -0.918012  31.544762   \n",
       "2021-06-14 00:00:00.400  245827973  5.029508 -2.619484 -0.593327  31.560478   \n",
       "2021-06-14 00:00:00.500  245827974  4.909807 -2.949019 -0.766270  31.576233   \n",
       "\n",
       "                         diag_sonic  volt_KH20  diag_kh20  amb_press  \\\n",
       "TIMESTAMP                                                              \n",
       "2021-06-14 00:00:00.100           0   34.73232          0   49.94021   \n",
       "2021-06-14 00:00:00.200           0   35.63925          0   49.94020   \n",
       "2021-06-14 00:00:00.300           0   34.99607          0   49.93660   \n",
       "2021-06-14 00:00:00.400           0   34.75459          0   49.92876   \n",
       "2021-06-14 00:00:00.500           0   34.93779          0   49.92938   \n",
       "\n",
       "                          TA_1_1_1  RH_1_1_1  FW  \n",
       "TIMESTAMP                                         \n",
       "2021-06-14 00:00:00.100  31.569908  12.12791 NaN  \n",
       "2021-06-14 00:00:00.200  31.566185  12.12012 NaN  \n",
       "2021-06-14 00:00:00.300  31.544762  12.12461 NaN  \n",
       "2021-06-14 00:00:00.400  31.560478  12.12626 NaN  \n",
       "2021-06-14 00:00:00.500  31.576233  12.12276 NaN  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "889262e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-25T18:22:25.968609Z",
     "start_time": "2021-05-25T18:22:25.950656Z"
    }
   },
   "source": [
    "# Using the Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4baaa79e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T14:17:53.001200Z",
     "start_time": "2021-08-05T14:17:52.983186Z"
    }
   },
   "outputs": [],
   "source": [
    "fluxcalc = ec.CalcFluxWithKH20()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dd29cb32-d6de-4172-8ec9-8cd2a5320f10",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dfsm = df.loc[pd.to_datetime(\"2009-05-27 16:00:00.000\"):pd.to_datetime(\"2009-05-27 17:00:00.000\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ab886212-1ca9-41d7-8d2d-80d491d16fe5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-01T18:54:36.307122Z",
     "start_time": "2021-07-01T18:54:20.653003Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\paulinkenbrandt\\.conda\\envs\\pygis39\\lib\\site-packages\\dateutil\\parser\\_parser.py:1213: UnknownTimezoneWarning: tzname MST identified but not understood.  Pass `tzinfos` argument in order to correctly return a timezone-aware datetime.  In a future version, this will raise an exception.\n",
      "  warnings.warn(\"tzname {tzname} identified but not understood.  \"\n"
     ]
    }
   ],
   "source": [
    "agmet = pd.read_csv(r\"C:\\Users\\paulinkenbrandt\\Downloads\\UCC_scan_2137_2021_06_08_1623185624\\UCC_scan_2137_2021_06_08_1623185624.csv\",\n",
    "            skiprows=14,na_values=['M','S'],index_col='Day',parse_dates=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5d03e90c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T14:28:53.783652Z",
     "start_time": "2021-08-05T14:28:53.753783Z"
    }
   },
   "outputs": [],
   "source": [
    "df['Ta_K'] = fluxcalc.convert_CtoK(df['TA_1_1_1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d95cdfbc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T14:31:42.409760Z",
     "start_time": "2021-08-05T14:29:52.484263Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='TIMESTAMP'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Ta_C'] = fluxcalc.convert_KtoC(df['Ta_K'])\n",
    "df['Ta_C'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "688abd23",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T14:26:15.829356Z",
     "start_time": "2021-08-05T14:25:15.773359Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\My Drive\\Python\\Pycharm\\MicroMet\\ec.py:174: RuntimeWarning: invalid value encountered in sqrt\n",
      "  StDevTa = np.sqrt(CovTs_Ts - 1.02 * df['Ts'].mean() * CovTs_Q - 0.2601 * QMSE * df['Ts'].mean()**2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-06-14 00:00:00.100000\n",
      "Ta          -264.656450\n",
      "Td           -19.702826\n",
      "D           -128.945297\n",
      "Ustr           0.287840\n",
      "zeta          23.851167\n",
      "H             -9.392055\n",
      "StDevUz        0.335854\n",
      "StDevTa             NaN\n",
      "direction    197.711613\n",
      "exchange      -6.856075\n",
      "lambdaE     -137.541783\n",
      "ET            -0.004599\n",
      "Uxy            4.688926\n",
      "dtype: float64\n",
      "2021-06-14 01:00:00\n",
      "Ta          -263.959940\n",
      "Td           -19.727427\n",
      "D           -128.673120\n",
      "Ustr           0.287332\n",
      "zeta          19.618621\n",
      "H             -7.691986\n",
      "StDevUz        0.325100\n",
      "StDevTa             NaN\n",
      "direction    197.191048\n",
      "exchange       8.845215\n",
      "lambdaE     -112.785248\n",
      "ET            -0.003783\n",
      "Uxy            4.220911\n",
      "dtype: float64\n",
      "2021-06-14 02:00:00\n",
      "Ta          -261.781061\n",
      "Td           -19.806437\n",
      "D           -127.802450\n",
      "Ustr           0.243579\n",
      "zeta          46.246937\n",
      "H            -10.970191\n",
      "StDevUz        0.296597\n",
      "StDevTa             NaN\n",
      "direction    216.378376\n",
      "exchange     -64.697553\n",
      "lambdaE     -160.221238\n",
      "ET            -0.005423\n",
      "Uxy            3.761789\n",
      "dtype: float64\n",
      "2021-06-14 03:00:00\n",
      "Ta          -259.317911\n",
      "Td           -19.901848\n",
      "D           -126.758092\n",
      "Ustr           0.515101\n",
      "zeta           3.948593\n",
      "H             -9.611317\n",
      "StDevUz        0.316423\n",
      "StDevTa             NaN\n",
      "direction    239.604942\n",
      "exchange     676.575700\n",
      "lambdaE     -148.412242\n",
      "ET            -0.005076\n",
      "Uxy            0.917504\n",
      "dtype: float64\n",
      "2021-06-14 04:00:00\n",
      "Ta          -257.886032\n",
      "Td           -19.960030\n",
      "D           -126.125002\n",
      "Ustr           0.328369\n",
      "zeta          -8.281680\n",
      "H              6.000309\n",
      "StDevUz        0.147094\n",
      "StDevTa             NaN\n",
      "direction    280.959847\n",
      "exchange    -230.759225\n",
      "lambdaE       90.375979\n",
      "ET             0.003110\n",
      "Uxy            0.292992\n",
      "dtype: float64\n",
      "2021-06-14 05:00:00\n",
      "Ta          -250.906191\n",
      "Td           -20.219669\n",
      "D           -123.334314\n",
      "Ustr           0.077007\n",
      "zeta        -310.889914\n",
      "H              2.809997\n",
      "StDevUz        0.083369\n",
      "StDevTa             NaN\n",
      "direction    246.923333\n",
      "exchange     -24.780340\n",
      "lambdaE       42.844665\n",
      "ET             0.001517\n",
      "Uxy            0.910437\n",
      "dtype: float64\n",
      "2021-06-14 06:00:00\n",
      "Ta          -250.022141\n",
      "Td           -20.255774\n",
      "D           -122.950671\n",
      "Ustr           0.183825\n",
      "zeta          42.089878\n",
      "H             -4.257381\n",
      "StDevUz        0.120134\n",
      "StDevTa             NaN\n",
      "direction    226.231610\n",
      "exchange      19.045451\n",
      "lambdaE      -64.091387\n",
      "ET            -0.002278\n",
      "Uxy            1.363632\n",
      "dtype: float64\n",
      "2021-06-14 07:00:00\n",
      "Ta           -260.374259\n",
      "Td            -19.889383\n",
      "D            -126.894092\n",
      "Ustr            0.759305\n",
      "zeta            2.324386\n",
      "H             -21.370625\n",
      "StDevUz         0.215214\n",
      "StDevTa              NaN\n",
      "direction     177.826847\n",
      "exchange    -2662.104930\n",
      "lambdaE      -304.655806\n",
      "ET             -0.010374\n",
      "Uxy             0.052767\n",
      "dtype: float64\n",
      "2021-06-14 08:00:00\n",
      "Ta          -265.604208\n",
      "Td           -19.670471\n",
      "D           -129.304062\n",
      "Ustr           0.223439\n",
      "zeta          66.836303\n",
      "H            -12.313317\n",
      "StDevUz        0.225677\n",
      "StDevTa             NaN\n",
      "direction    158.196418\n",
      "exchange     404.460173\n",
      "lambdaE     -181.585059\n",
      "ET            -0.006047\n",
      "Uxy            1.028745\n",
      "dtype: float64\n",
      "2021-06-14 09:00:00\n",
      "Ta          -266.963768\n",
      "Td           -19.622091\n",
      "D           -129.842182\n",
      "Ustr           0.220622\n",
      "zeta         123.623004\n",
      "H            -21.697412\n",
      "StDevUz        0.268399\n",
      "StDevTa             NaN\n",
      "direction    201.652747\n",
      "exchange     -42.067660\n",
      "lambdaE     -315.456714\n",
      "ET            -0.010445\n",
      "Uxy            2.591526\n",
      "dtype: float64\n",
      "2021-06-14 10:00:00\n",
      "Ta          -267.940472\n",
      "Td           -19.588346\n",
      "D           -130.218712\n",
      "Ustr           0.312792\n",
      "zeta          38.090403\n",
      "H            -19.092180\n",
      "StDevUz        0.385170\n",
      "StDevTa             NaN\n",
      "direction    225.539465\n",
      "exchange    -299.319837\n",
      "lambdaE     -275.197988\n",
      "ET            -0.009074\n",
      "Uxy            2.967277\n",
      "dtype: float64\n",
      "2021-06-14 11:00:00\n",
      "Ta          -268.411888\n",
      "Td           -19.572633\n",
      "D           -130.394368\n",
      "Ustr           0.306077\n",
      "zeta          47.724718\n",
      "H            -22.349139\n",
      "StDevUz        0.407010\n",
      "StDevTa             NaN\n",
      "direction    217.476118\n",
      "exchange      85.604038\n",
      "lambdaE     -322.651065\n",
      "ET            -0.010617\n",
      "Uxy            2.702133\n",
      "dtype: float64\n",
      "2021-06-14 12:00:00\n",
      "Ta          -268.730090\n",
      "Td           -19.561770\n",
      "D           -130.515941\n",
      "Ustr           0.346095\n",
      "zeta          41.423814\n",
      "H            -28.058724\n",
      "StDevUz        0.495019\n",
      "StDevTa             NaN\n",
      "direction    197.630740\n",
      "exchange    -209.048087\n",
      "lambdaE     -406.160971\n",
      "ET            -0.013346\n",
      "Uxy            3.748823\n",
      "dtype: float64\n",
      "2021-06-14 13:00:00\n",
      "Ta          -269.101333\n",
      "Td           -19.549620\n",
      "D           -130.652025\n",
      "Ustr           0.491651\n",
      "zeta          10.334480\n",
      "H            -20.499591\n",
      "StDevUz        0.557729\n",
      "StDevTa             NaN\n",
      "direction    207.884720\n",
      "exchange     -34.624154\n",
      "lambdaE     -295.563909\n",
      "ET            -0.009696\n",
      "Uxy            4.292809\n",
      "dtype: float64\n",
      "2021-06-14 14:00:00\n",
      "Ta          -269.443513\n",
      "Td           -19.538175\n",
      "D           -130.780330\n",
      "Ustr           0.371039\n",
      "zeta          18.446457\n",
      "H            -15.523515\n",
      "StDevUz        0.546588\n",
      "StDevTa             NaN\n",
      "direction    231.693557\n",
      "exchange    -112.869206\n",
      "lambdaE     -223.719382\n",
      "ET            -0.007329\n",
      "Uxy            4.730830\n",
      "dtype: float64\n",
      "2021-06-14 15:00:00\n",
      "Ta          -269.566595\n",
      "Td           -19.533971\n",
      "D           -130.827497\n",
      "Ustr           0.444369\n",
      "zeta          13.346330\n",
      "H            -19.392977\n",
      "StDevUz        0.589977\n",
      "StDevTa             NaN\n",
      "direction    252.592799\n",
      "exchange     385.796062\n",
      "lambdaE     -279.527648\n",
      "ET            -0.009152\n",
      "Uxy            5.191002\n",
      "dtype: float64\n",
      "2021-06-14 16:00:00\n",
      "Ta          -269.690928\n",
      "Td           -19.530021\n",
      "D           -130.871810\n",
      "Ustr           0.515861\n",
      "zeta           8.623029\n",
      "H            -19.812724\n",
      "StDevUz        0.602612\n",
      "StDevTa             NaN\n",
      "direction    238.706039\n",
      "exchange     310.665733\n",
      "lambdaE     -285.743388\n",
      "ET            -0.009350\n",
      "Uxy            5.499004\n",
      "dtype: float64\n",
      "2021-06-14 17:00:00\n",
      "Ta          -269.633914\n",
      "Td           -19.531977\n",
      "D           -130.849866\n",
      "Ustr           0.518190\n",
      "zeta           9.896190\n",
      "H            -22.970767\n",
      "StDevUz        0.583047\n",
      "StDevTa             NaN\n",
      "direction    244.632424\n",
      "exchange     258.311755\n",
      "lambdaE     -331.105131\n",
      "ET            -0.010837\n",
      "Uxy            5.871996\n",
      "dtype: float64\n",
      "2021-06-14 18:00:00\n",
      "Ta          -269.548255\n",
      "Td           -19.534677\n",
      "D           -130.819572\n",
      "Ustr           0.414383\n",
      "zeta          13.103687\n",
      "H            -15.468672\n",
      "StDevUz        0.552955\n",
      "StDevTa             NaN\n",
      "direction    249.213353\n",
      "exchange     391.808898\n",
      "lambdaE     -223.037947\n",
      "ET            -0.007303\n",
      "Uxy            5.940251\n",
      "dtype: float64\n",
      "2021-06-14 19:00:00\n",
      "Ta          -269.238920\n",
      "Td           -19.545036\n",
      "D           -130.703398\n",
      "Ustr           0.422230\n",
      "zeta           8.904318\n",
      "H            -11.249036\n",
      "StDevUz        0.517063\n",
      "StDevTa             NaN\n",
      "direction    256.711933\n",
      "exchange    -136.977552\n",
      "lambdaE     -162.569734\n",
      "ET            -0.005330\n",
      "Uxy            5.834362\n",
      "dtype: float64\n",
      "2021-06-14 20:00:00\n",
      "Ta          -268.170906\n",
      "Td           -19.582054\n",
      "D           -130.289023\n",
      "Ustr           0.254473\n",
      "zeta          19.573966\n",
      "H             -5.457889\n",
      "StDevUz        0.327946\n",
      "StDevTa             NaN\n",
      "direction    212.702367\n",
      "exchange    -660.421365\n",
      "lambdaE      -77.918667\n",
      "ET            -0.002567\n",
      "Uxy            3.430634\n",
      "dtype: float64\n",
      "2021-06-14 21:00:00\n",
      "Ta          -266.558682\n",
      "Td           -19.638841\n",
      "D           -129.655648\n",
      "Ustr           0.231843\n",
      "zeta          54.430000\n",
      "H            -11.495338\n",
      "StDevUz        0.287543\n",
      "StDevTa             NaN\n",
      "direction    190.096927\n",
      "exchange    -196.760956\n",
      "lambdaE     -169.337189\n",
      "ET            -0.005616\n",
      "Uxy            3.695470\n",
      "dtype: float64\n",
      "2021-06-14 22:00:00\n",
      "Ta          -264.671675\n",
      "Td           -19.705881\n",
      "D           -128.911474\n",
      "Ustr           0.210054\n",
      "zeta          81.538012\n",
      "H            -12.784553\n",
      "StDevUz        0.286028\n",
      "StDevTa             NaN\n",
      "direction    243.360832\n",
      "exchange     874.165136\n",
      "lambdaE     -188.679231\n",
      "ET            -0.006309\n",
      "Uxy            2.124624\n",
      "dtype: float64\n",
      "2021-06-14 23:00:00\n",
      "Ta           -264.534890\n",
      "Td            -19.717502\n",
      "D            -128.782870\n",
      "Ustr            0.159269\n",
      "zeta          281.267047\n",
      "H             -20.304647\n",
      "StDevUz         0.305575\n",
      "StDevTa              NaN\n",
      "direction     257.351787\n",
      "exchange     1594.387613\n",
      "lambdaE      -301.690402\n",
      "ET             -0.010093\n",
      "Uxy             1.805089\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\paulinkenbrandt\\.conda\\envs\\pygis39\\lib\\site-packages\\pandas\\core\\frame.py:8525: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  base_cov = np.cov(mat.T, ddof=ddof)\n",
      "C:\\Users\\paulinkenbrandt\\.conda\\envs\\pygis39\\lib\\site-packages\\numpy\\lib\\function_base.py:2493: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  c *= np.true_divide(1, fact)\n",
      "C:\\Users\\paulinkenbrandt\\.conda\\envs\\pygis39\\lib\\site-packages\\numpy\\lib\\function_base.py:2493: RuntimeWarning: invalid value encountered in multiply\n",
      "  c *= np.true_divide(1, fact)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-06-15 00:00:00\n",
      "Ta          -259.902402\n",
      "Td           -19.868884\n",
      "D           -127.118034\n",
      "Ustr                NaN\n",
      "zeta                NaN\n",
      "H                   NaN\n",
      "StDevUz             NaN\n",
      "StDevTa             NaN\n",
      "direction    309.692799\n",
      "exchange            NaN\n",
      "lambdaE             NaN\n",
      "ET                  NaN\n",
      "Uxy            2.167230\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "outdf = {}\n",
    "for i, g in df.groupby(pd.Grouper(freq='1H')):\n",
    "    outdf[g.first_valid_index()] = fluxcalc.runall(g)\n",
    "    print(g.first_valid_index())\n",
    "    print(outdf[g.first_valid_index()])\n",
    "    #print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8399ddec-2285-48b8-9933-3a61bae94cab",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-01T19:13:45.562561Z",
     "start_time": "2021-07-01T19:13:45.236433Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.concat(outdf,axis=1).T['ET'].plot()#.to_clipboardboard()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9d01ef5d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-01T22:54:48.114399Z",
     "start_time": "2021-06-01T22:54:47.334739Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.concat(outdf,axis=1).T.to_csv(\"G:/My Drive/Python/Pycharm/MicroMet/output.csv\")#.reset_index().set_index('Firstdate')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8ed6434-a5a8-4fbe-b93a-cc684cf3d990",
   "metadata": {
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "# Step by Step"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a051df6-b3e1-45e0-a179-cdcb9d86a3bf",
   "metadata": {},
   "source": [
    "## Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8b49aa62-4b10-461b-a97a-59cbf45e8174",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T13:40:04.437294Z",
     "start_time": "2021-08-05T13:40:04.425326Z"
    }
   },
   "outputs": [],
   "source": [
    "Rv = 461.51 # 'Water Vapor Gas Constant', 'J/[kg*K]'\n",
    "Ru = 8.314 # 'Universal Gas Constant', 'J/[kg*K]'\n",
    "Cpd = 1005 # 'Specific Heat of Dry Air', 'J/[kg*K]'\n",
    "Rd = 287.05 # 'Dry Air Gas Constant', 'J/[kg*K]'\n",
    "Co = 0.21  # Molar Fraction of Oxygen in the Atmosphere\n",
    "Mo = 0.032  # Molar Mass of Oxygen (gO2/mole)\n",
    "\n",
    "XKH20 = 1.412 # 'Path Length of KH20', 'cm'\n",
    "XKwC1 = -0.152214126 # First Order Coefficient in Vapor Density-KH20 Output Relationship, cm\n",
    "XKwC2 = -0.001667836 # Second Order Coefficient in Vapor Density-KH20 Output Relationship, cm\n",
    "directionKH20_U = 180\n",
    "UHeight = 3 # Height of Sonic Anemometer above Ground Surface', 'm'\n",
    "PathKH20_U = 0.1 # Separation Distance Between Sonic Anemometer and KH20', 'm', 0.1\n",
    "lag = 10 # number of lags to consider\n",
    "direction_bad_min = 0 # Clockwise Orientation from DirectionKH20_U\n",
    "direction_bad_max = 360 # Clockwise Orientation from DirectionKH20_U\n",
    "\n",
    "Kw = 1 # Extinction Coefficient of Water (m^3/[g*cm]) -instrument calibration\n",
    "Ko = -0.0045 # Extinction Coefficient of Oxygen (m^3/[g*cm]) -derived experimentally"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f465916b-7939-48d6-9e11-ae4a458b4d96",
   "metadata": {},
   "source": [
    "## Despike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fb9dc15a-de12-4df8-a77c-626d75a27296",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T13:40:08.988681Z",
     "start_time": "2021-08-05T13:40:08.968777Z"
    }
   },
   "outputs": [],
   "source": [
    "def despike(arr, nstd=4.5):\n",
    "    \"\"\"Removes spikes from parameter within a specified deviation from the mean.\n",
    "    \"\"\"\n",
    "    stdd = np.nanstd(arr) * nstd\n",
    "    avg = np.nanmean(arr)\n",
    "    avgdiff = stdd - np.abs(arr - avg)\n",
    "    y = np.where(avgdiff >= 0, arr, np.NaN)\n",
    "    nans, x = np.isnan(y), lambda z: z.nonzero()[0]\n",
    "    if len(x(~nans)) > 0:\n",
    "        y[nans] = np.interp(x(nans), x(~nans), y[~nans])\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "71cf02a8-ecbe-465c-baba-15caadd36101",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T13:40:10.639971Z",
     "start_time": "2021-08-05T13:40:10.622020Z"
    }
   },
   "outputs": [],
   "source": [
    "def renamedf(df):\n",
    "    return df.rename(columns={'T_SONIC':'Ts',\n",
    "                              'TA_1_1_1':'Ta',\n",
    "                              'amb_press':'Pr',\n",
    "                              'RH_1_1_1':'Rh',\n",
    "                              't_hmp':'Ta',\n",
    "                              'e_hmp':'Ea',\n",
    "                              'kh':'volt_KH20'\n",
    "                              })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2ec04b6f-9732-4cc0-938a-56746a068a34",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T13:40:14.454490Z",
     "start_time": "2021-08-05T13:40:14.448505Z"
    }
   },
   "outputs": [],
   "source": [
    "#dfsm = df.loc[pd.to_datetime(\"2009-05-27 16:00:00.000\"):pd.to_datetime(\"2009-05-27 17:00:00.000\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7e1ca943-2008-4d73-a329-cc58a8fb423f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T13:40:18.398466Z",
     "start_time": "2021-08-05T13:40:18.152125Z"
    }
   },
   "outputs": [],
   "source": [
    "dfsm = renamedf(df)\n",
    "\n",
    "despikefields = ['Ux', 'Uy', 'Uz', 'Ts', 'LnKH', 'Pr', 'Ta', 'Rh']\n",
    "\n",
    "dfsm['LnKH'] = np.log(dfsm['volt_KH20'].to_numpy())\n",
    "\n",
    "\n",
    "for col in despikefields:\n",
    "    if col in dfsm.columns:\n",
    "        dfsm[col] = despike(dfsm[col].to_numpy(), nstd=4.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5e7bd66-c9dd-4b72-a18a-07e783f87762",
   "metadata": {},
   "source": [
    "## CSAT Correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e05c2486-e0fa-4a1c-ab48-5c11e1c61bbf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T13:40:23.410728Z",
     "start_time": "2021-08-05T13:40:23.392776Z"
    }
   },
   "outputs": [],
   "source": [
    "def fix_csat(Ux, Uy, Uz):\n",
    "\n",
    "    CSAT3Inverse = [[-0.5, 0, 0.86602540378444],\n",
    "                    [0.25, 0.4330127018922, 0.86602540378444],\n",
    "                    [0.25, -0.4330127018922, 0.86602540378444]]\n",
    "    CSAT3Transform = [[-1.3333333333333, 0.66666666666666, 0.66666666666666],\n",
    "                      [0, 1.1547005383792, -1.1547005383792],\n",
    "                      [0.3849001794597, 0.3849001794597, 0.3849001794597]]\n",
    "\n",
    "    Ux_out = []\n",
    "    Uy_out = []\n",
    "    Uz_out = []\n",
    "\n",
    "    for i in range(len(Ux)):\n",
    "        u = {}\n",
    "        u[0] = CSAT3Inverse[0][0] * Ux[i] + CSAT3Inverse[0][1] * Uy[i] + CSAT3Inverse[0][2] * Uz[i]\n",
    "        u[1] = CSAT3Inverse[1][0] * Ux[i] + CSAT3Inverse[1][1] * Uy[i] + CSAT3Inverse[1][2] * Uz[i]\n",
    "        u[2] = CSAT3Inverse[2][0] * Ux[i] + CSAT3Inverse[2][1] * Uy[i] + CSAT3Inverse[2][2] * Uz[i]\n",
    "\n",
    "        scalar = (Ux[i] ** 2. + Uy[i] ** 2. + Uz[i] ** 2.) ** 0.5\n",
    "\n",
    "        u[0] = u[0] / (0.68 + 0.32 * np.sin(np.arccos(u[0] / scalar)))\n",
    "        u[1] = u[1] / (0.68 + 0.32 * np.sin(np.arccos(u[1] / scalar)))\n",
    "        u[2] = u[2] / (0.68 + 0.32 * np.sin(np.arccos(u[2] / scalar)))\n",
    "\n",
    "        Ux_out.append(CSAT3Transform[0][0] * u[0] + CSAT3Transform[0][1] * u[1] + CSAT3Transform[0][2] * u[2])\n",
    "        Uy_out.append(CSAT3Transform[1][0] * u[0] + CSAT3Transform[1][1] * u[1] + CSAT3Transform[1][2] * u[2])\n",
    "        Uz_out.append(CSAT3Transform[2][0] * u[0] + CSAT3Transform[2][1] * u[1] + CSAT3Transform[2][2] * u[2])\n",
    "\n",
    "    return Ux_out, Uy_out, Uz_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eb7770d1-f3c9-466c-b762-74a55ac0e6a6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T13:40:24.151855Z",
     "start_time": "2021-08-05T13:40:24.148864Z"
    }
   },
   "outputs": [],
   "source": [
    "def convert_KtoC(T):\n",
    "    return T - 273.16\n",
    "\n",
    "def convert_CtoK(T):\n",
    "    return T + 273.16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f212c93f-4005-4754-a89d-9826e56f5bb0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T13:40:35.700071Z",
     "start_time": "2021-08-05T13:40:24.755414Z"
    }
   },
   "outputs": [],
   "source": [
    "dfsm['Ts'] = convert_CtoK(dfsm['Ts'].to_numpy())\n",
    "\n",
    "dfsm['Ux'],dfsm['Uy'],dfsm['Uz'] = fix_csat(dfsm['Ux'].to_numpy(),\n",
    "                                           dfsm['Uy'].to_numpy(),\n",
    "                                           dfsm['Uz'].to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "684dc9e0-e20b-45ee-9b57-3504c3db8e95",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T13:42:27.706581Z",
     "start_time": "2021-08-05T13:40:35.763902Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='TIMESTAMP'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfsm['Ts'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0912be6-8c9b-4f0c-bd6a-f401876b73fe",
   "metadata": {},
   "source": [
    "## Weather Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f38f7457-86cc-40e1-b086-32fd85a862c6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T13:42:33.171525Z",
     "start_time": "2021-08-05T13:42:33.164534Z"
    }
   },
   "outputs": [],
   "source": [
    "def calculated_parameters(df):\n",
    "    df['pV'] = calc_pV(df['Ea'], df['Ts'])\n",
    "    df['Tsa'] = calc_Tsa(df['Ts'], df['Pr'], df['pV'])\n",
    "    df['E'] = calc_E(df['pV'], df['Tsa'])\n",
    "    df['Q'] = calc_Q(df['Pr'], df['E'])\n",
    "    df['Sd'] = calc_Q(df['Pr'], calc_Es(df['Tsa'])) - df['Q']\n",
    "    return df\n",
    "\n",
    "def calc_pV(Ea, Ts):\n",
    "    return (Ea * 1000.0) / (Rv * Ts)\n",
    "\n",
    "def calc_Tsa(Ts, P, pV, Rv=461.51):\n",
    "    E = pV * Rv * Ts\n",
    "    return -0.01645278052 * (\n",
    "                -500 * P - 189 * E + np.sqrt(250000 * P ** 2 + 128220 * E * P + 35721 * E ** 2)) / pV / Rv\n",
    "\n",
    "def calc_E(pV, T):\n",
    "    return pV * Rv * T\n",
    "\n",
    "def calc_Q(P, E):\n",
    "    return (0.622 * E) / (P - 0.378 * E)\n",
    "\n",
    "def calc_Es(T):\n",
    "    g0 = -2836.5744\n",
    "    g1 = -6028.076559\n",
    "    g2 = 19.54263612\n",
    "    g3 = -0.02737830188\n",
    "    g4 = 0.000016261698\n",
    "    g5 = 0.00000000070229056\n",
    "    g6 = -0.00000000000018680009\n",
    "    g7 = 2.7150305\n",
    "\n",
    "    return np.exp(\n",
    "        g0 * T ** (-2) + g1 * T ** (-1) + g2 + g3 * T + g4 * T ** 2 + g5 * T ** 3 + g6 * T ** 4 + g7 * np.log(T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc681509",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9fa0b7f6-b7cd-45ed-9d37-85e8179b39da",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-05T13:42:34.799112Z",
     "start_time": "2021-08-05T13:42:34.675201Z"
    }
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Ea'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\.conda\\envs\\pygis39\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3079\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3080\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3081\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Ea'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-22-9dac49fd7d90>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdfsm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalculated_parameters\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdfsm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-21-844daf0d14e7>\u001b[0m in \u001b[0;36mcalculated_parameters\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcalculated_parameters\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'pV'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalc_pV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Ea'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Ts'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Tsa'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalc_Tsa\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Ts'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Pr'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'pV'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'E'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalc_E\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'pV'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Tsa'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Q'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalc_Q\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Pr'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'E'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\pygis39\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3022\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3023\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3024\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3025\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3026\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\pygis39\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3080\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3081\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3082\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3083\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3084\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Ea'"
     ]
    }
   ],
   "source": [
    "dfsm = calculated_parameters(dfsm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b9a9677-a2fb-4a32-9398-18c605882278",
   "metadata": {},
   "source": [
    "## Calculate the Correct XKw Value for KH20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2dbc2f19-583a-4d2b-8429-b7be28a0657c",
   "metadata": {},
   "outputs": [],
   "source": [
    "XKw = XKwC1 + 2 * XKwC2 * (dfsm['pV'].mean() * 1000.)\n",
    "Kw = XKw / XKH20"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f6474a3-bed4-4615-8b0a-ba1da3ff3184",
   "metadata": {},
   "source": [
    "## Covariances"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89b3ef7d-d0f8-4195-b2cb-788e053d6357",
   "metadata": {},
   "source": [
    "### regular covariance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d607bbde-dd60-4342-b077-41c8f142d30c",
   "metadata": {},
   "outputs": [],
   "source": [
    "CovTs_Ts = dfsm[['Ts', 'Ts']].cov().iloc[0,0] # location index needed because of same fields\n",
    "CovUx_Uy = dfsm[['Ux', 'Uy']].cov().loc['Ux', 'Uy']  # CalcCovariance(IWP.Ux, IWP.Uy)\n",
    "CovUx_Uz = dfsm[['Ux', 'Uz']].cov().loc['Ux', 'Uz']  # CalcCovariance(IWP.Ux, IWP.Uz)\n",
    "CovUy_Uz = dfsm[['Uy', 'Uz']].cov().loc['Uy', 'Uz']  # CalcCovariance(IWP.Uy, IWP.Uz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e44c0dc1-1066-45eb-bc58-9d27e79ac2b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CovTs_Ts = 0.07064076201818181\n",
      "CovUx_Uy = 0.23705990519123227\n",
      "CovUx_Uz = 0.004523740457045727\n",
      "CovUy_Uz = -0.17724196577590595\n"
     ]
    }
   ],
   "source": [
    "print(f\"CovTs_Ts = {CovTs_Ts}\") \n",
    "print(f\"CovUx_Uy = {CovUx_Uy}\")\n",
    "print(f\"CovUx_Uz = {CovUx_Uz}\")\n",
    "print(f\"CovUy_Uz = {CovUy_Uz}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4a5e045-384f-4237-80d5-4df9c5953f7f",
   "metadata": {},
   "source": [
    "0.24682559155736247, ux_uy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b6495c0d-2f1e-41b4-a397-2a20c67ef53a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated covariance matrix:\n",
      "MCD (Robust):\n",
      "[[0.93628763 0.33968448]\n",
      " [0.33968448 1.5743863 ]]\n",
      "MLE:\n",
      "[[1.26745461 0.23705332]\n",
      " [0.23705332 1.92521684]]\n"
     ]
    }
   ],
   "source": [
    "covdata = dfsm[['Ux', 'Uy']].to_numpy()\n",
    "\n",
    "# fit a MCD robust estimator to data\n",
    "robust_cov = MinCovDet().fit(covdata)\n",
    "# fit a MLE estimator to data\n",
    "emp_cov = EmpiricalCovariance().fit(covdata)\n",
    "print('Estimated covariance matrix:\\n'\n",
    "      'MCD (Robust):\\n{}\\n'\n",
    "      'MLE:\\n{}'.format(robust_cov.covariance_, emp_cov.covariance_))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3a665fc-7625-4c86-971e-43fdba52f1ea",
   "metadata": {},
   "source": [
    "### Max covariance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "662b1d06-a46a-4684-96a3-1b1d8b79a704",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_max_covariance(df, colx, coly, lags=10):\n",
    "    dfcov = []\n",
    "    for i in np.arange(-1 * lags, lags):\n",
    "\n",
    "        df[f\"{coly}_{i}\"] = df[coly].shift(i)\n",
    "        dfcov.append(df[[colx, f\"{coly}_{i}\"]].cov().loc[colx, f\"{coly}_{i}\"])\n",
    "        #print(i,df[[colx, f\"{coly}_{i}\"]].cov().loc[colx, f\"{coly}_{i}\"])\n",
    "        df = df.drop([f\"{coly}_{i}\"], axis=1)\n",
    "\n",
    "    abscov = np.abs(dfcov)\n",
    "    maxabscov = np.max(abscov)\n",
    "    try:\n",
    "        print(np.where(abscov == maxabscov)[0])\n",
    "        maxlagindex = np.where(abscov == maxabscov)[0][0]\n",
    "        lagno = maxlagindex - lags\n",
    "        maxcov = dfcov[maxlagindex]\n",
    "    except IndexError:\n",
    "        lagno = 0\n",
    "        maxcov = dfcov[10]\n",
    "    return maxcov, lagno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "35476943-e306-4cc5-ac7d-925eb68e68d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19]\n",
      "[9]\n",
      "[0]\n",
      "[10]\n",
      "[10]\n",
      "[10]\n",
      "[8]\n",
      "[9]\n",
      "[0]\n",
      "[10]\n",
      "[11]\n",
      "[11]\n",
      "[10]\n"
     ]
    }
   ],
   "source": [
    "lag = 10\n",
    "CovTs_Q = calc_max_covariance(dfsm, 'Ts', 'Q', lag)[0]\n",
    "CovUx_LnKH = calc_max_covariance(dfsm, 'Ux', 'LnKH', lag)[0]\n",
    "CovUx_Q = calc_max_covariance(dfsm, 'Ux', 'Q', lag)[0]\n",
    "CovUx_Sd = calc_max_covariance(dfsm, 'Ux', 'Sd', lag)[0]\n",
    "CovUx_Ts = calc_max_covariance(dfsm, 'Ux', 'Ts', lag)[0]\n",
    "CovUy_LnKH = calc_max_covariance(dfsm, 'Uy', 'LnKH', lag)[0]\n",
    "CovUy_Q = calc_max_covariance(dfsm, 'Uy', 'Q', lag)[0]\n",
    "CovUy_Sd = calc_max_covariance(dfsm, 'Uy', 'Sd', lag)[0]\n",
    "CovUy_Ts = calc_max_covariance(dfsm, 'Uy', 'Ts', lag)[0]\n",
    "CovUz_LnKH = calc_max_covariance(dfsm, 'Uz', 'LnKH', lag)[0]\n",
    "CovUz_Q = calc_max_covariance(dfsm, 'Uz', 'Q', lag)[0]\n",
    "CovUz_Sd = calc_max_covariance(dfsm, 'Uz', 'Sd', lag)[0]\n",
    "CovUz_Ts = calc_max_covariance(dfsm, 'Uz', 'Ts', lag)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5302e78e-1064-41c9-8d39-3cc77a624ba2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CovTs_Q = 2.6988572225757985e-05\n",
      "CovUx_LnKH = -0.0063005465373859836\n",
      "CovUx_Q = 2.1757598999650003e-05\n",
      "CovUx_Sd = -7.645342713314233e-05\n",
      "CovUx_Ts = -0.04646998062382586\n",
      "CovUy_LnKH = 0.10860722132220826\n",
      "CovUy_Q = -0.00037125529443951153\n",
      "CovUy_Sd = 0.00035091036903631145\n",
      "CovUy_Ts = -0.0784617900114158\n",
      "CovUz_LnKH = -0.029794698347107507\n"
     ]
    }
   ],
   "source": [
    "print(f\"CovTs_Q = {CovTs_Q}\") \n",
    "print(f\"CovUx_LnKH = {CovUx_LnKH}\")\n",
    "print(f\"CovUx_Q = {CovUx_Q}\")\n",
    "print(f\"CovUx_Sd = {CovUx_Sd}\")\n",
    "print(f\"CovUx_Ts = {CovUx_Ts}\")\n",
    "print(f\"CovUy_LnKH = {CovUy_LnKH}\")\n",
    "print(f\"CovUy_Q = {CovUy_Q}\")\n",
    "print(f\"CovUy_Sd = {CovUy_Sd}\")\n",
    "print(f\"CovUy_Ts = {CovUy_Ts}\")\n",
    "print(f\"CovUz_LnKH = {CovUz_LnKH}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d7306fa0-84e2-4569-8dd2-135facb66219",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-7.645342713314233e-05, 0)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc_max_covariance(dfsm, 'Ux', 'Sd', lag)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecbf98f8-da6e-4912-acf4-c642a9c7ecd7",
   "metadata": {},
   "source": [
    "## Coordinate Rotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7733f8d1-ebb5-4483-95e8-46c6d4bab30e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def coord_rotation(df, Ux='Ux', Uy='Uy', Uz='Uz'):\n",
    "    \"\"\"Traditional Coordinate Rotation\n",
    "    \"\"\"\n",
    "    xmean = df[Ux].mean()\n",
    "    ymean = df[Uy].mean()\n",
    "    zmean = df[Uz].mean()\n",
    "    Uxy = np.sqrt(xmean ** 2 + ymean ** 2)\n",
    "    Uxyz = np.sqrt(xmean ** 2 + ymean ** 2 + zmean ** 2)\n",
    "    cosν = xmean / Uxy\n",
    "    sinν = ymean / Uxy\n",
    "    sinTheta = zmean / Uxyz\n",
    "    cosTheta = Uxy / Uxyz\n",
    "    return cosν, sinν, sinTheta, cosTheta, Uxy, Uxyz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c72c45a3-86c4-4fea-9044-cdb4cc567c63",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b851182a-c6e0-48f0-91bd-525b83d0c2f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Traditional Coordinate Rotation\n",
    "cosν, sinν, sinTheta, cosTheta, Uxy, Uxyz = coord_rotation(dfsm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1df1569c-98ca-458f-a287-af17d8e1f3a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "de045dfa-3717-4967-b47d-7f4a055588ef",
   "metadata": {},
   "source": [
    "## Mean Squared Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e39e7fec-51ef-41f7-844d-a7344e749600",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_MSE(y):\n",
    "    return np.mean((y - np.mean(y)) ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6e4aae44-0da9-4ecd-b7f9-f895efb00f6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the Mean Squared Error of Velocity Components and Humidity\n",
    "UxMSE = calc_MSE(dfsm['Ux'])\n",
    "UyMSE = calc_MSE(dfsm['Uy'])\n",
    "UzMSE = calc_MSE(dfsm['Uz'])\n",
    "QMSE = calc_MSE(dfsm['Q'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b686192-dcde-4197-8388-59e8c84f79f4",
   "metadata": {},
   "source": [
    "## Correct Covariances for Coordinate Rotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fbd34432-0216-4216-b1ae-54c0f98e22be",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Uz_Ts = CovUz_Ts * cosTheta - CovUx_Ts * sinTheta * cosν - CovUy_Ts * sinTheta * sinν\n",
    "if np.abs(Uz_Ts) >= np.abs(CovUz_Ts):\n",
    "    CovUz_Ts = Uz_Ts\n",
    "\n",
    "Uz_LnKH = CovUz_LnKH * cosTheta - CovUx_LnKH * sinTheta * cosν - CovUy_LnKH * sinν * sinTheta\n",
    "if np.abs(Uz_LnKH) >= np.abs(CovUz_LnKH):\n",
    "    CovUz_LnKH = Uz_LnKH\n",
    "CovUx_Q = CovUx_Q * cosTheta * cosν + CovUy_Q * cosTheta * sinν + CovUz_Q * sinTheta\n",
    "CovUy_Q = CovUy_Q * cosν - CovUx_Q * sinν\n",
    "CovUz_Q = CovUz_Q * cosTheta - CovUx_Q * sinTheta * cosν - CovUy_Q * sinν * sinTheta\n",
    "CovUx_Uz = CovUx_Uz * cosν * (cosTheta**2 - sinTheta**2) - 2 * CovUx_Uy * sinTheta * cosTheta * sinν * cosν + CovUy_Uz * sinν * (cosTheta**2 - sinTheta**2) - UxMSE * sinTheta * cosTheta * cosν**2 - UyMSE * sinTheta * cosTheta * sinν**2 + UzMSE * sinTheta * cosTheta\n",
    "CovUy_Uz = CovUy_Uz * cosTheta * cosν - CovUx_Uz * cosTheta * sinν - CovUx_Uy * sinTheta * (cosν**2 - sinν**2) + UxMSE * sinTheta * sinν * cosν - UyMSE * sinTheta * sinν * cosν\n",
    "CovUz_Sd = CovUz_Sd * cosTheta - CovUx_Sd * sinTheta * cosν - CovUy_Sd * sinν * sinTheta\n",
    "Uxy_Uz = np.sqrt(CovUx_Uz**2 + CovUy_Uz**2)\n",
    "Ustr = np.sqrt(Uxy_Uz)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47a9910e-2c40-4446-bb7f-1662a77935b0",
   "metadata": {},
   "source": [
    "## Find Average Air Temperature From Average Sonic Temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6474fea7-71d8-4e88-93d8-8789f0fc541e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_Tsa(Ts, P, pV, Rv=461.51):\n",
    "    E = pV * Rv * Ts\n",
    "    return -0.01645278052 * (\n",
    "                -500 * P - 189 * E + np.sqrt(250000 * P ** 2 + 128220 * E * P + 35721 * E ** 2)) / pV / Rv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dcb4b6eb-30d4-4150-bbe1-2f221df875c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tsa = calc_Tsa(dfsm['Ts'].mean(), dfsm['Pr'].mean(), dfsm['pV'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48a4be38-a883-458c-86b8-57fc5bca2684",
   "metadata": {},
   "source": [
    "## Calculate the Latent Heat of Vaporization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a2c17029-5c9d-4cd1-baa6-97656169ccfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "lamb = (2500800 - 2366.8 * (convert_KtoC(Tsa)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9de1a926-87bd-45ac-8342-7739cdccaae4",
   "metadata": {},
   "source": [
    "## Determine Vertical Wind and Water Vapor Density Covariance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bfa8c05b-958e-4534-9538-d31351e72d43",
   "metadata": {},
   "outputs": [],
   "source": [
    "Uz_pV = (CovUz_LnKH / XKw) / 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf631f31-aadb-4341-9f61-0ec61e5c9e50",
   "metadata": {},
   "source": [
    "## Calculate the Correct Average Values of Some Key Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6ee49c6f-8526-4839-9663-7e672a1a9fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "Cp = Cpd * (1 + 0.84 * dfsm['Q'].mean())\n",
    "pD = (dfsm['Pr'].mean() - dfsm['E'].mean()) / (Rd * Tsa)\n",
    "p = pD + dfsm['pV'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d468a5ec-fa40-4353-b60f-40758f4b8a5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7df8ff29-a0a4-4b14-aad7-dc2d592ef7a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "StDevTa = np.sqrt(CovTs_Ts - 1.02 * dfsm['Ts'].mean() * CovTs_Q - 0.2601 * QMSE * dfsm['Ts'].mean()**2)\n",
    "Uz_Ta = CovUz_Ts - 0.07 * lamb * Uz_pV / (p * Cp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b117bf59-1d6b-4342-83f3-995d6f52433c",
   "metadata": {},
   "source": [
    "## Determine Saturation Vapor Pressure of the Air Using Highly Accurate Wexler's Equations Modified by Hardy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ecd9355d-b8ce-4f4f-b028-d6fd7aed2919",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_Td(E):\n",
    "    c0 = 207.98233\n",
    "    c1 = -20.156028\n",
    "    c2 = 0.46778925\n",
    "    c3 = -0.0000092288067\n",
    "    d0 = 1\n",
    "    d1 = -0.13319669\n",
    "    d2 = 0.0056577518\n",
    "    d3 = -0.000075172865\n",
    "    lne = np.log(E)\n",
    "    return (c0 + c1 * lne + c2 * lne ** 2 + c3 * lne ** 3) / (d0 + d1 * lne + d2 * lne ** 2 + d3 * lne ** 3)\n",
    "\n",
    "def calc_Es(T):\n",
    "    g0 = -2836.5744\n",
    "    g1 = -6028.076559\n",
    "    g2 = 19.54263612\n",
    "    g3 = -0.02737830188\n",
    "    g4 = 0.000016261698\n",
    "    g5 = 0.00000000070229056\n",
    "    g6 = -0.00000000000018680009\n",
    "    g7 = 2.7150305\n",
    "\n",
    "    return np.exp(g0 * T ** (-2) + g1 * T ** (-1) + g2 + g3 * T + g4 * T ** 2 + g5 * T ** 3 + g6 * T ** 4 + g7 * np.log(T))\n",
    "\n",
    "def calc_Q(P, E):\n",
    "    return (0.622 * E) / (P - 0.378 * E)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ddb23328-a1e2-47bc-b060-f89f17c180a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Td = calc_Td(dfsm['E'].mean())\n",
    "D = calc_Es(Tsa) - dfsm['E'].mean()\n",
    "S = (calc_Q(dfsm['Pr'].mean(), calc_Es(Tsa + 1)) - calc_Q(dfsm['Pr'].mean(), calc_Es(Tsa - 1))) / 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf6ca13f-5981-42e9-af2a-8cd71a311dc1",
   "metadata": {},
   "source": [
    "## Determine Wind Direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "25fe8d4c-49cc-471f-bf7e-eef461c4497a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "WindDirection = np.arctan(df['Uy'].mean() / df['Ux'].mean()) * 180 / np.pi\n",
    "if df['Ux'].mean() < 0:\n",
    "    WindDirection += 180 * np.sign(df['Uy'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "45217c94-9561-4a5f-b7ad-585eb102bce0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "direction = directionKH20_U - WindDirection\n",
    "\n",
    "if direction < 0:\n",
    "    direction += 360"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74ab09e2-4010-4d78-af19-fbdb1d17332a",
   "metadata": {},
   "source": [
    "## Calculate the Lateral Separation Distance Projected Into the Mean Wind Direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "df0cdc8a-8c7d-4bd9-936a-4c2f4cb9579b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pathlen = PathKH20_U * np.abs(np.sin((np.pi / 180) * direction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f13aac75-eb66-4a59-b7a1-475d7080b2ae",
   "metadata": {},
   "source": [
    "## Calculate the Average and Standard Deviations of the Rotated Velocity Components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "844acd5c-8417-4d57-a405-9d58016f329c",
   "metadata": {},
   "outputs": [],
   "source": [
    "StDevUz = dfsm['Uz'].std()\n",
    "UMean = dfsm['Ux'].mean() * cosTheta * cosν + dfsm['Uy'].mean() * cosTheta * sinν + dfsm['Uz'].mean() * sinTheta"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a61ec340-fdb4-4a40-a35e-2cfb844fcb98",
   "metadata": {},
   "source": [
    "## Frequency Response Corrections (Massman, 2000 & 2001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0be9a351-f2ee-46a6-918a-a2f2d15b4e06",
   "metadata": {},
   "outputs": [],
   "source": [
    "tauB = (3600) / 2.8\n",
    "tauEKH20 = np.sqrt((0.01 / (4 * UMean)) **2 + (pathlen / (1.1 * UMean))**2)\n",
    "tauETs = np.sqrt((0.1 / (8.4 * UMean))**2)\n",
    "tauEMomentum = np.sqrt((0.1 / (5.7 * UMean))**2 + (0.1 / (2.8 * UMean))**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7187c6fe-5728-4f9f-bf5f-63fd705f0826",
   "metadata": {},
   "source": [
    "## Calculate ζ and Correct Values of Uᕽ and Uz_Ta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1780cd27-0c05-4058-a228-289fbe8e7514",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_L(Ust, Tsa, Uz_Ta):\n",
    "    #removed negative sign\n",
    "    return -1*(Ust ** 3) * Tsa / (9.8 * 0.4 * Uz_Ta)\n",
    "\n",
    "def calc_AlphX(UHeight, L):\n",
    "    if (UHeight / L) <= 0:\n",
    "        alph = 0.925\n",
    "        X = 0.085\n",
    "    else:\n",
    "        alph = 1\n",
    "        X = 2 - 1.915 / (1 + 0.5 * UHeight / L)\n",
    "    return alph, X\n",
    "\n",
    "def correct_spectral(B, alpha, varib):\n",
    "    B_alpha = B ** alpha\n",
    "    V_alpha = varib ** alpha\n",
    "    return (B_alpha / (B_alpha + 1)) * (B_alpha / (B_alpha + V_alpha)) * (1 / (V_alpha + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8065a972-9fa9-4e5e-9714-8bad76a57323",
   "metadata": {},
   "outputs": [],
   "source": [
    "L = calc_L(Ustr, Tsa, Uz_Ta)\n",
    "alpha, X = calc_AlphX(UHeight, L)\n",
    "fX = X * UMean / UHeight\n",
    "B = 2 * np.pi * fX * tauB\n",
    "momentum = 2 * np.pi * fX * tauEMomentum\n",
    "_Ts = 2 * np.pi * fX * tauETs\n",
    "_KH20 = 2 * np.pi * fX * tauEKH20\n",
    "Ts = correct_spectral(B, alpha, _Ts)\n",
    "Uxy_Uz /= correct_spectral(B, alpha, momentum)\n",
    "Ustr = np.sqrt(Uxy_Uz)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7300e17-8665-495a-ae16-4da5002c88f7",
   "metadata": {},
   "source": [
    "## Recalculate L With New Uᕽ and Uz_Ta, and Calculate High Frequency Corrections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3d2af8c7-a250-4437-a768-98be6e9ce5bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "L = calc_L(Ustr, Tsa, Uz_Ta / Ts)\n",
    "alpha, X = calc_AlphX(UHeight, L)\n",
    "Ts = correct_spectral(B, alpha, _Ts)\n",
    "KH20 = correct_spectral(B, alpha, _KH20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c285682-1784-42be-9780-57bb2b334b36",
   "metadata": {},
   "source": [
    "## Correct the Covariance Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b17af44b-1009-4017-bde2-538adb79177d",
   "metadata": {},
   "outputs": [],
   "source": [
    "Uz_Ta /= Ts\n",
    "Uz_pV /= KH20\n",
    "Uxy_Uz /= correct_spectral(B, alpha, momentum)\n",
    "Ustr = np.sqrt(Uxy_Uz)\n",
    "CovUz_Sd /= KH20\n",
    "exchange = ((p * Cp) / (S + Cp / lamb)) * CovUz_Sd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ab403d2-b085-4b7b-b3ce-8d0fbbd3ff8d",
   "metadata": {},
   "source": [
    "## KH20 Oxygen Correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "9fdf0432-0737-4e58-b40b-3dc0cc6502ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def correct_KH20(Uz_Ta, P, T, Mo, Co, Ru, Ko, Kw):\n",
    "    \"\"\"Calculates an additive correction for the KH20 due to cross sensitivity between H20 and 02 molecules.\n",
    "    Uz_Ta = Covariance of Vertical Wind Component and Air Temperature (m*K/s)\n",
    "    P = Air Pressure (Pa)\n",
    "    T = Air Temperature (K)\n",
    "    Kw = Extinction Coefficient of Water (m^3/[g*cm]) -instrument calibration\n",
    "    Ko = Extinction Coefficient of Oxygen (m^3/[g*cm]) -derived experimentally\n",
    "    returns KH20 Oxygen Correction\n",
    "    \"\"\"\n",
    "    return ((Co * Mo * P) / (Ru * T ** 2)) * (Ko / Kw) * Uz_Ta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "daa7bd4f-3455-440d-ba6f-6452a1fea2bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "Uz_pV += correct_KH20(Uz_Ta, dfsm['Pr'].mean(), Tsa, Mo, Co, Ru, Ko, Kw)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56cac4fa-ece7-4746-9621-fb7395631728",
   "metadata": {},
   "source": [
    "## Calculate New H and LE Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "bcaad807-cebe-4b2a-a86a-fbd152b513a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "H = p * Cp * Uz_Ta\n",
    "lambdaE = lamb * Uz_pV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d6650e8-6cee-4f19-af32-ce3e6e6ebcb5",
   "metadata": {},
   "source": [
    "## Webb, Pearman and Leuning Correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4b876d20-8c7e-49f3-b184-9d5c5cfdef7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "lambdaE = lamb * p * Cp * Tsa * (1.0 + (1.0 / 0.622) * (dfsm['pV'].mean() / pD)) * (Uz_pV + (dfsm['pV'].mean() / Tsa) * Uz_Ta) / (p * Cp * Tsa + lamb * (1.0 + (1 / 0.622) * (dfsm['pV'].mean() / pD)) * dfsm['pV'].mean() * 0.07)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53c6f717-206f-4231-8804-51a97852d659",
   "metadata": {},
   "source": [
    "## Finish Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "de73680f-a31b-4156-8e3f-71111c10d348",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_water_density(temperature):\n",
    "    d1 = -3.983035  # °C\n",
    "    d2 = 301.797  # °C\n",
    "    d3 = 522528.9  # °C2\n",
    "    d4 = 69.34881  # °C\n",
    "    d5 = 999.97495  # kg/m3\n",
    "    return d5 * (1 - (temperature + d1) ** 2 * (temperature + d2) / (d3 * (temperature + d4)))  # 'kg/m^3\n",
    "\n",
    "def calc_latent_heat_of_vaporization(temperature):\n",
    "    l0 = 2500800\n",
    "    l1 = -2360\n",
    "    l2 = 1.6\n",
    "    l3 = -0.06\n",
    "    return l0 + l1 * temperature + l2 * temperature ** 2 + l3 * temperature ** 3  # 'J/kg\n",
    "\n",
    "def get_Watts_to_H2O_conversion_factor(temperature, day_fraction):\n",
    "    to_inches = 25.4\n",
    "    return (calc_water_density(temperature) * 86.4 * day_fraction) / (\n",
    "            calc_latent_heat_of_vaporization(temperature) * to_inches)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "f3af7dac-7e20-42ef-a9ed-3bee56b32af3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ta             23.188007\n",
       "Td              4.865331\n",
       "D            1979.929389\n",
       "Ustr            0.527603\n",
       "zeta            0.009941\n",
       "H             -37.058966\n",
       "StDevUz         0.428887\n",
       "StDevTa         0.216468\n",
       "direction     118.360800\n",
       "exchange      -48.227787\n",
       "lambdaE       431.413364\n",
       "ET              0.024934\n",
       "Uxy             3.152308\n",
       "dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tsa = convert_KtoC(Tsa)\n",
    "Td = convert_KtoC(Td)\n",
    "zeta = UHeight / L\n",
    "ET = lambdaE * get_Watts_to_H2O_conversion_factor(Tsa, (dfsm.last_valid_index() - dfsm.first_valid_index())/ pd.to_timedelta(1, unit='D'))\n",
    "#'Out.Parameters = CWP\n",
    "columns = ['Ta','Td','D', 'Ustr', 'zeta', 'H', 'StDevUz', 'StDevTa',  'direction', 'exchange', 'lambdaE', 'ET', 'Uxy']\n",
    "out = [Tsa, Td, D, Ustr, zeta, H, StDevUz, StDevTa,  direction, exchange,  lambdaE, ET, Uxy]\n",
    "pd.Series(data=out,index=columns)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# Extra Equations"
   ]
  },
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   "id": "7140853d-bc04-4ec4-8044-185c1c15a489",
   "metadata": {},
   "source": [
    "-7.4889559330869538E-05, ux_xd"
   ]
  },
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   "source": [
    "## KH20 Conversions"
   ]
  },
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   "source": [
    "KH20 Manual: https://s.campbellsci.com/documents/us/manuals/kh20.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c222de83",
   "metadata": {},
   "source": [
    "KH20 Calibration Params"
   ]
  },
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   "source": [
    "kh20sn = 1778\n",
    "vapor_density_gpm3 = [1.76,3.06,4.17,5.45,6.82,8.07,9.20,10.48,11.82,13.02,14.19,15.49,16.83,17.72,18.08]\n",
    "kh20_output_lnmv = [8.32,7.99,7.76,7.48,7.19,6.92,6.70,6.44,6.17,5.93,5.71,5.44,5.13,4.95,4.88]\n",
    "\n",
    "\n",
    "import statsmodels.api as sm\n",
    "\n",
    "y = vapor_density_gpm3\n",
    "x = kh20_output_lnmv\n",
    "X = sm.add_constant(x)\n",
    "model = sm.OLS(y,X)\n",
    "results = model.fit()\n",
    "intercept, slope = results.params\n",
    "print(f\"slope = {slope}\\nintercept={intercept}\")\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x,[i*slope+intercept for i in x], color='red')\n",
    "\n",
    "df['LnKH'] = np.log(df['volt_KH20'])\n",
    "df['vap_density_gm3'] = df['LnKH']*slope + intercept"
   ]
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   "source": [
    "parameters = {\n",
    "    'Ea': ['Actual Vapor Pressure', 'kPa'],\n",
    "    'LnKH': ['Natural Log of Krypton Hygrometer Output', 'mV'],\n",
    "    'Pr': ['Air Pressure', 'Pa'],\n",
    "    'Ta': ['Air Temperature', 'K'],\n",
    "    'Ts': ['Sonic Temperature', 'K'],\n",
    "    'Ux': ['X Component of Wind Speed', 'm/s'],\n",
    "    'Uy': ['Y Component of Wind Speed', 'm/s'],\n",
    "    'Uz': ['Z Component of Wind Speed', 'm/s'],\n",
    "    'X': ['Path Length of KH20', 'cm', 1.412],\n",
    "    'XKwC1': ['First Order Coefficient in Vapor Density-KH20 Output Relationship (lnMV = C0 + C1 * VD + C2 * VD ^ 2, Windows Clean/Full Vapor Range)','cm', -0.152214126],\n",
    "    'XKwC2': ['Second Order Coefficient in Vapor Density-KH20 Output Relationship (lnMV = C0 + C1 * VD + C2 * VD ^ 2, Windows Clean/Full Vapor Range)','cm', -0.001667836],\n",
    "    'DirectionKH20_U': ['Clockwise Orientation of Sonic Anemometer from Northern Azimuth (degrees)',180],\n",
    "    'DirectionBadMin': ['Clockwise Orientation from DirectionKH20_U Where Unusable Wind Direction Data Begins','degrees', 0],\n",
    "    'DirectionBadMax': ['Clockwise Orientation from DirectionKH20_U Where Unusable Wind Direction Data Ends','degrees', 0],\n",
    "    'UHeight': ['Height of Sonic Anemometer above Ground Surface', 'm', 3],\n",
    "    'PathKH20_U':['Separation Distance Between Sonic Anemometer and KH20', 'm', 0.1],\n",
    "    'lag': ['Number of Shifts to Achieve Maximum Cross Correlation (Applied in Both Directions)','count', 10],\n",
    "    'E':['Vapor Pressure','kPa'],\n",
    "    'Q':['Specific Humidity','unitless'],'pV':['Water Vapor Density','kg/m^3'],\n",
    "    'Sd':['Entropy of Dry Air','J/K'],\n",
    "    'Tsa':['Absolute Air Temperature Derived from Sonic Temperature','K'], \n",
    "    \n",
    "}"
   ]
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     "start_time": "2021-05-24T21:42:52.237538Z"
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   "outputs": [],
   "source": [
    "\n"
   ]
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   "execution_count": null,
   "id": "295ac080",
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   "source": []
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