reconstruct_L4A_AGBD_L2A_metrics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "13d0e745",
   "metadata": {},
   "source": [
    "# Reproducing L4A AGBD estimates from GEDI L2A RH metrics \n",
    "This tutorial shows how to reconstruct L4A aboveground biomass density (AGBD) estimates using L2A relative height (RH) metrics. We will use a GEDI L4A file [GEDI04_A_2020207182449_O09168_03_T03028_02_002_02_V002.h5](https://daac.ornl.gov/daacdata/gedi/GEDI_L4A_AGB_Density_V2_1/data/GEDI04_A_2020207182449_O09168_03_T03028_02_002_02_V002.h5) and corresponding GEDI L2A file [GEDI02_A_2020207182449_O09168_03_T03028_02_003_01_V002.h5](https://e4ftl01.cr.usgs.gov/GEDI/GEDI02_A.002/2020.07.25/GEDI02_A_2020207182449_O09168_03_T03028_02_003_01_V002.h5) for this purpose.\n",
    "\n",
    "First, we will reconstruct L4A AGBD value for a single shot to describe the steps, and then we will do it for all the shots within a single beam. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eda1fbdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all the modules\n",
    "from os import path\n",
    "import h5py\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style='whitegrid')\n",
    "\n",
    "# GEDI L4a file\n",
    "l4a = 'GEDI04_A_2020207182449_O09168_03_T03028_02_002_02_V002.h5'\n",
    "l4af = path.join('full_orbits', l4a)\n",
    "# GEDI L2a file\n",
    "l2a = 'GEDI02_A_2020207182449_O09168_03_T03028_02_003_01_V002.h5'\n",
    "l2af = path.join('full_orbits', l2a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56313317",
   "metadata": {},
   "source": [
    "## 1. Reconstruct AGBD for a single shot\n",
    "We will use a single shot (shot number `91680600300633870`) from the beam `BEAM0110`. First, open the L4A file, and find out the predict_stratum and selected_algorithm for the shot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "148b86a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For GEDI shot number 91680600300633870, the predict stratum is EBT_SAs, and the selected algorithm group is 2.\n"
     ]
    }
   ],
   "source": [
    "# read the L4A file\n",
    "hf_l4a = h5py.File(l4af, 'r')\n",
    "\n",
    "sn = 158560 # index of shot 91680600300633870\n",
    "beam = 'BEAM0110' # selected beam\n",
    "shot_number_l4a = hf_l4a[beam]['shot_number'][sn:sn+1][0]\n",
    "predict_stratum_l4a = hf_l4a[beam]['predict_stratum'][sn:sn+1][0]\n",
    "selected_algorithm_l4a = hf_l4a[beam]['selected_algorithm'][sn:sn+1][0]\n",
    "\n",
    "print_s = f\"\"\"For GEDI shot number {shot_number_l4a}, \\\n",
    "the predict stratum is {predict_stratum_l4a.decode('UTF-8')}, \\\n",
    "and the selected algorithm group is {selected_algorithm_l4a}.\"\"\"\n",
    "print(print_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e89880bf",
   "metadata": {},
   "source": [
    "**L4A model parameters**: Let's print the `model_data` information for the `EBT_SAs` stratum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ebf84781",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predict_stratum  :  b'EBT_SAs'\n",
      "model_group  :  4\n",
      "model_name  :  b'EBT_coarse'\n",
      "model_id  :  1\n",
      "x_transform  :  b'sqrt'\n",
      "y_transform  :  b'sqrt'\n",
      "bias_correction_name  :  b'Snowdon'\n",
      "fit_stratum  :  b'EBT'\n",
      "rh_index  :  [50 98  0  0  0  0  0  0]\n",
      "predictor_id  :  [1 2 0 0 0 0 0 0]\n",
      "predictor_max_value  :  [12.66807  13.052969  0.        0.        0.        0.        0.\n",
      "  0.      ]\n",
      "vcov  :  [[ 1.6210171  -0.10003732 -0.047196    0.          0.        ]\n",
      " [-0.10003732  0.05625523 -0.04469026  0.          0.        ]\n",
      " [-0.047196   -0.04469026  0.04664591  0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.        ]]\n",
      "par  :  [-104.9654541     6.80217409    3.95531225    0.            0.        ]\n",
      "rse  :  3.9209127\n",
      "dof  :  4811\n",
      "response_max_value  :  1578.0\n",
      "bias_correction_value  :  1.1133657\n",
      "npar  :  3\n"
     ]
    }
   ],
   "source": [
    "model_data_l4a = hf_l4a['ANCILLARY']['model_data']\n",
    "model_stratum_l4a = model_data_l4a[12] # EBT_SAs\n",
    "for v in model_stratum_l4a.dtype.names:\n",
    "    print(v, \" : \", model_stratum_l4a[v])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dc3db51",
   "metadata": {},
   "source": [
    "A square-root transformation is applied to both predictor and response variables as indicated by `x_transform` and `y_transform` above. Take a note of the `bias_correction_value` and `bias_correction_name` above. We will use the `bias_correction_value` later to back-transform AGBD values. Now, read all the model parameters and print the AGBD model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e8c9e8c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predict stratum EBT_SAs uses following model:\n",
      "AGBD = -104.9654541015625 + 6.802174091339111 x RH_50 + 3.9553122520446777 x  RH_98\n"
     ]
    }
   ],
   "source": [
    "intercept = model_stratum_l4a['par'][0] # intercept\n",
    "coeff_1 = model_stratum_l4a['par'][1] # first coeff\n",
    "coeff_2 = model_stratum_l4a['par'][2] # first coeff\n",
    "predict_1 = model_stratum_l4a['rh_index'][0] # predictor 1\n",
    "predict_2 = model_stratum_l4a['rh_index'][1] # predictor 1\n",
    "# print\n",
    "print_s = f\"\"\"The predict stratum {predict_stratum_l4a.decode('UTF-8')} uses following model:\n",
    "AGBD = {intercept} + {coeff_1} x RH_{predict_1} + {coeff_2} x  RH_{predict_2}\"\"\"\n",
    "print(print_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf9da11b",
   "metadata": {},
   "source": [
    "Also, read the predictor and response offsets from the L4A file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a757b531",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predictor offset is 100, \n",
      "and the response offset is 0\n"
     ]
    }
   ],
   "source": [
    "predictor_offset = hf_l4a[beam]['agbd_prediction'].attrs['predictor_offset']\n",
    "response_offset = hf_l4a[beam]['agbd_prediction'].attrs['response_offset']\n",
    "# print\n",
    "print_s = f\"\"\"The predictor offset is {predictor_offset}, \n",
    "and the response offset is {response_offset}\"\"\"\n",
    "print(print_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8264644",
   "metadata": {},
   "source": [
    "We will use the predictor offset later when transforming the variables. \n",
    "\n",
    "**L2A file and RH metrics**\n",
    "\n",
    "Now, let's open the L2A file and read the RH metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d1a8897b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For GEDI shot number 91680600300633870, \n",
      "L2A rh_50 is 19.149999618530273 m.\n",
      "L2A rh_98 is 37.150001525878906 m.\n",
      "Selected algorithm group is 2.\n"
     ]
    }
   ],
   "source": [
    "l2af = path.join('full_orbits', l2a)\n",
    "hf_l2a = h5py.File(l2af, 'r')\n",
    "\n",
    "rh_50 = hf_l2a[beam]['rh'][sn:sn+1,50][0] # rh_50\n",
    "rh_98 = hf_l2a[beam]['rh'][sn:sn+1,98][0] # rh_98\n",
    "rh_units = hf_l2a[beam]['rh'].attrs['units']\n",
    "shot_number_l2a = hf_l2a[beam]['shot_number'][sn:sn+1][0]\n",
    "selected_algorithm_l2a = hf_l2a[beam]['selected_algorithm'][sn:sn+1][0]\n",
    "\n",
    "print_s = f\"\"\"For GEDI shot number {shot_number_l2a}, \n",
    "L2A rh_50 is {rh_50} {rh_units}.\n",
    "L2A rh_98 is {rh_98} {rh_units}.\n",
    "Selected algorithm group is {selected_algorithm_l2a}.\"\"\"\n",
    "print(print_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6172ea73",
   "metadata": {},
   "source": [
    "**xvar**\n",
    "\n",
    "We can now compute predictor variables `xvar` from the L2A RH metrics and compare them with the L4A `xvar` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "23c91967",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For GEDI shot number 91680600300633870,\n",
      "L2A derived xvars: 10.91558517068738, 11.711105905331012.\n",
      "L4A xvars: 10.9155855178833, 11.711106300354004 \n"
     ]
    }
   ],
   "source": [
    "xvar_1_l2a = np.sqrt(rh_50 + predictor_offset) # sqrt x transformation\n",
    "xvar_2_l2a = np.sqrt(rh_98 + predictor_offset) # sqrt x transformation\n",
    "xvar_1_l4a = hf_l4a[beam]['xvar'][158560:158561,0][0]\n",
    "xvar_2_l4a = hf_l4a[beam]['xvar'][158560:158561,1][0]\n",
    "print_s = f\"\"\"For GEDI shot number {shot_number_l2a},\n",
    "L2A derived xvars: {xvar_1_l2a}, {xvar_2_l2a}.\n",
    "L4A xvars: {xvar_1_l4a}, {xvar_2_l4a} \"\"\"\n",
    "print(print_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2436f4eb",
   "metadata": {},
   "source": [
    "**agbd_t**\n",
    "\n",
    "Let's use the L2A-derived xvars from the above to compute AGBD in the transformed space and compare the transformed AGBD with that of L4A."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1261ddab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For GEDI shot number 91680600300633870,\n",
      "L2A derived agbd_t: 15.605337210641139.\n",
      "L4A agbd_t: 15.605341134767514 \n"
     ]
    }
   ],
   "source": [
    "agbd_t_l4a = intercept + (coeff_1 * xvar_1_l4a) + (coeff_2 *  xvar_2_l4a) # l2a agbd_t\n",
    "agbd_t_l2a = intercept + (coeff_1 * xvar_1_l2a) + (coeff_2 *  xvar_2_l2a) # l2a agbd_t\n",
    "\n",
    "print_s = f\"\"\"For GEDI shot number {shot_number_l2a},\n",
    "L2A derived agbd_t: {agbd_t_l2a}.\n",
    "L4A agbd_t: {agbd_t_l4a} \"\"\"\n",
    "print(print_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99fe29e3",
   "metadata": {},
   "source": [
    "**agbd**\n",
    "\n",
    "Now, let's back-transform L2A derived `agbd_t` and compare it with the AGBD from L4A."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d08706d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For GEDI shot number 91680600300633870,\n",
      "L2A derived estimated AGBD is 271.13409507246865 Mg / ha.\n",
      "L4A estimated AGBD 271.1342314315326 Mg / ha.\n"
     ]
    }
   ],
   "source": [
    "agbd_units = hf_l4a[beam]['agbd'].attrs['units'] \n",
    "bias_correction_value = model_stratum_l4a['bias_correction_value'] # bias_correction_value\n",
    "\n",
    "agbd_l2a = (agbd_t_l2a**2) * bias_correction_value # snowdon model with sqrt y transform\n",
    "agbd_l4a = (agbd_t_l4a**2) * bias_correction_value # snowdon model with sqrt y transform\n",
    "\n",
    "# closing files\n",
    "hf_l4a.close()\n",
    "hf_l2a.close()\n",
    "\n",
    "print_s = f\"\"\"For GEDI shot number {shot_number_l2a},\n",
    "L2A derived estimated AGBD is {agbd_l2a} {agbd_units}.\n",
    "L4A estimated AGBD {agbd_l4a} {agbd_units}.\"\"\"\n",
    "print(print_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0bcbe0d",
   "metadata": {},
   "source": [
    "## 2. Reconstruct AGBD for a single beam\n",
    "We will reconstruct AGBD for all the shots within beam `BEAM0110`. Let's open the L4A file and read model parameters into a pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c1659b79",
   "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>bias_correction_name</th>\n",
       "      <th>bias_correction_value</th>\n",
       "      <th>x_transform</th>\n",
       "      <th>y_transform</th>\n",
       "      <th>npar</th>\n",
       "      <th>par</th>\n",
       "      <th>rh_index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>predict_stratum</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>DBT_Af</th>\n",
       "      <td>Snowdon</td>\n",
       "      <td>1.092463</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>[-118.40806579589844, 1.956794023513794, 9.961...</td>\n",
       "      <td>[50, 98, 0, 0, 0, 0, 0, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBT_Au</th>\n",
       "      <td>Snowdon</td>\n",
       "      <td>1.017920</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>[-155.41419982910156, 7.816701889038086, 7.709...</td>\n",
       "      <td>[70, 98, 0, 0, 0, 0, 0, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBT_Eu</th>\n",
       "      <td>Snowdon</td>\n",
       "      <td>0.962632</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>[-96.53070068359375, 7.175395488739014, 2.9214...</td>\n",
       "      <td>[70, 98, 0, 0, 0, 0, 0, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBT_NAs</th>\n",
       "      <td>Snowdon</td>\n",
       "      <td>1.016632</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>[-110.05912780761719, 5.133802890777588, 6.171...</td>\n",
       "      <td>[60, 98, 0, 0, 0, 0, 0, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBT_SA</th>\n",
       "      <td>Snowdon</td>\n",
       "      <td>1.105528</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>[-134.77015686035156, 6.653591632843018, 6.687...</td>\n",
       "      <td>[50, 98, 0, 0, 0, 0, 0, 0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                bias_correction_name  bias_correction_value x_transform  \\\n",
       "predict_stratum                                                           \n",
       "DBT_Af                       Snowdon               1.092463        sqrt   \n",
       "DBT_Au                       Snowdon               1.017920        sqrt   \n",
       "DBT_Eu                       Snowdon               0.962632        sqrt   \n",
       "DBT_NAs                      Snowdon               1.016632        sqrt   \n",
       "DBT_SA                       Snowdon               1.105528        sqrt   \n",
       "\n",
       "                y_transform  npar  \\\n",
       "predict_stratum                     \n",
       "DBT_Af                 sqrt     3   \n",
       "DBT_Au                 sqrt     3   \n",
       "DBT_Eu                 sqrt     3   \n",
       "DBT_NAs                sqrt     3   \n",
       "DBT_SA                 sqrt     3   \n",
       "\n",
       "                                                               par  \\\n",
       "predict_stratum                                                      \n",
       "DBT_Af           [-118.40806579589844, 1.956794023513794, 9.961...   \n",
       "DBT_Au           [-155.41419982910156, 7.816701889038086, 7.709...   \n",
       "DBT_Eu           [-96.53070068359375, 7.175395488739014, 2.9214...   \n",
       "DBT_NAs          [-110.05912780761719, 5.133802890777588, 6.171...   \n",
       "DBT_SA           [-134.77015686035156, 6.653591632843018, 6.687...   \n",
       "\n",
       "                                   rh_index  \n",
       "predict_stratum                              \n",
       "DBT_Af           [50, 98, 0, 0, 0, 0, 0, 0]  \n",
       "DBT_Au           [70, 98, 0, 0, 0, 0, 0, 0]  \n",
       "DBT_Eu           [70, 98, 0, 0, 0, 0, 0, 0]  \n",
       "DBT_NAs          [60, 98, 0, 0, 0, 0, 0, 0]  \n",
       "DBT_SA           [50, 98, 0, 0, 0, 0, 0, 0]  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the L4A file\n",
    "hf_l4a = h5py.File(l4af, 'r')\n",
    "\n",
    "# model_parameters\n",
    "model_data=hf_l4a['ANCILLARY']['model_data']\n",
    "predict_stratum = model_data['predict_stratum'].astype('U13')\n",
    "bias_correction_name = model_data['bias_correction_name'].astype('U13')\n",
    "bias_correction_value = model_data['bias_correction_value']\n",
    "x_transform = model_data['x_transform'].astype('U13')\n",
    "y_transform = model_data['y_transform'].astype('U13')\n",
    "npar = model_data['npar']\n",
    "par = model_data['par']\n",
    "rh_index = model_data['rh_index']\n",
    "\n",
    "# pandas dataframe\n",
    "df_l4a_model = pd.DataFrame(list(zip(predict_stratum, bias_correction_name, bias_correction_value,\n",
    "                                    x_transform,y_transform, npar, par, rh_index)), \n",
    "                     columns=['predict_stratum', 'bias_correction_name', 'bias_correction_value',\n",
    "                                    'x_transform', 'y_transform', 'npar', 'par', 'rh_index'])\n",
    "df_l4a_model.set_index('predict_stratum', inplace=True)\n",
    "\n",
    "# print the header rows\n",
    "df_l4a_model.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7c58b7e",
   "metadata": {},
   "source": [
    "Now, read the data from `BEAM0110` into the pandas dataframe `df_l4a`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d7ac33c9",
   "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>agbd_l4a</th>\n",
       "      <th>selected_algorithm_l4a</th>\n",
       "      <th>predict_stratum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shot_number_l4a</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00475310</th>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>ENT_Eu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00475311</th>\n",
       "      <td>25.602335</td>\n",
       "      <td>1</td>\n",
       "      <td>ENT_Eu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00475312</th>\n",
       "      <td>24.043011</td>\n",
       "      <td>1</td>\n",
       "      <td>ENT_Eu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00475313</th>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>ENT_Eu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00475314</th>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>ENT_Eu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    agbd_l4a  selected_algorithm_l4a predict_stratum\n",
       "shot_number_l4a                                                     \n",
       "00475310        -9999.000000                       2          ENT_Eu\n",
       "00475311           25.602335                       1          ENT_Eu\n",
       "00475312           24.043011                       1          ENT_Eu\n",
       "00475313        -9999.000000                       2          ENT_Eu\n",
       "00475314        -9999.000000                       1          ENT_Eu"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shot_number_l4a = hf_l4a[beam]['shot_number'] # selected algorithm\n",
    "agbd_l4a = hf_l4a[beam]['agbd'] # l4a agbd\n",
    "selected_algorithm_l4a = hf_l4a[beam]['selected_algorithm'] # selected algorithm\n",
    "predict_stratum = hf_l4a[beam]['predict_stratum'] # selected algorithm\n",
    "\n",
    "df_l4a = pd.DataFrame(list(zip(shot_number_l4a[:], agbd_l4a[:], selected_algorithm_l4a[:], \n",
    "                               predict_stratum[:].astype('U13'))), \n",
    "                     columns=['shot_number_l4a', 'agbd_l4a', 'selected_algorithm_l4a', 'predict_stratum'])\n",
    "df_l4a['shot_number_l4a'] = df_l4a['shot_number_l4a'].astype(str).str[-8:]\n",
    "df_l4a = df_l4a.set_index('shot_number_l4a')\n",
    "\n",
    "df_l4a.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6653fd8b",
   "metadata": {},
   "source": [
    "We can plot total shots by the prediction strata to check how many prediction strata are in the beam `BEAM0110`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "15c13e71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_l4a.predict_stratum.value_counts().plot(kind='bar',  ylabel='Total Shots', figsize=(20, 4))\n",
    "ax.set_title(f'Total shots by prediction strata in {beam}')\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "598482dc",
   "metadata": {},
   "source": [
    "There are ten prediction strata in the `BEAM0110`. Let's also plot total shots by the selected algorithm setting groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b7885d80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_l4a.selected_algorithm_l4a.value_counts().plot(kind='bar', ylabel='Total Shots', figsize=(20, 4))\n",
    "ax.set_title(f'Total shots by selected algorithm setting groups in {beam}')\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edafa0ca",
   "metadata": {},
   "source": [
    "There are four algorithm setting groups selected for the beam `BEAM0110`. Now, open the corresponding GEDI L2A file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "75ae1364",
   "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>rh_a1</th>\n",
       "      <th>rh_a2</th>\n",
       "      <th>rh_a3</th>\n",
       "      <th>rh_a4</th>\n",
       "      <th>rh_a5</th>\n",
       "      <th>rh_a6</th>\n",
       "      <th>rh_a10</th>\n",
       "      <th>selected_algorithm_l2a</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shot_number_l2a</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>00475310</th>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[-1.08, -1.04, -1.0, -0.97, -0.93, -0.89, -0.8...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[-1.75, -1.68, -1.6, -1.53, -1.45, -1.38, -1.3...</td>\n",
       "      <td>[-0.33, -0.33, -0.29, -0.29, -0.26, -0.22, -0....</td>\n",
       "      <td>[-4.177726745605469, -4.1077267456054685, -4.0...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00475311</th>\n",
       "      <td>[-1.19, -1.15, -1.12, -1.08, -1.04, -1.04, -1....</td>\n",
       "      <td>[-2.35, -2.27, -2.16, -2.09, -2.01, -1.94, -1....</td>\n",
       "      <td>[-1.26, -1.23, -1.19, -1.15, -1.15, -1.12, -1....</td>\n",
       "      <td>[-1.19, -1.19, -1.15, -1.12, -1.08, -1.08, -1....</td>\n",
       "      <td>[-2.87, -2.68, -2.53, -2.39, -2.27, -2.16, -2....</td>\n",
       "      <td>[-1.97, -1.94, -1.86, -1.79, -1.75, -1.68, -1....</td>\n",
       "      <td>[-2.87, -2.68, -2.53, -2.39, -2.27, -2.16, -2....</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00475312</th>\n",
       "      <td>[-0.97, -0.97, -0.93, -0.89, -0.85, -0.85, -0....</td>\n",
       "      <td>[-2.46, -2.39, -2.31, -2.24, -2.16, -2.09, -2....</td>\n",
       "      <td>[-1.19, -1.19, -1.15, -1.12, -1.08, -1.08, -1....</td>\n",
       "      <td>[-0.97, -0.97, -0.93, -0.93, -0.89, -0.89, -0....</td>\n",
       "      <td>[-3.06, -2.95, -2.8, -2.68, -2.57, -2.46, -2.3...</td>\n",
       "      <td>[-2.01, -1.97, -1.9, -1.86, -1.83, -1.75, -1.7...</td>\n",
       "      <td>[-3.06, -2.95, -2.8, -2.68, -2.57, -2.46, -2.3...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00475313</th>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[-1.34, -1.3, -1.26, -1.23, -1.19, -1.15, -1.1...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[-0.33, -0.29, -0.22, -0.18, -0.11, -0.03, 0.0...</td>\n",
       "      <td>[-0.18, -0.18, -0.18, -0.14, -0.14, -0.14, -0....</td>\n",
       "      <td>[-3.1685696411132813, -3.1285696411132813, -3....</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00475314</th>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[158.7861328125, 158.7861328125, 158.786132812...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             rh_a1  \\\n",
       "shot_number_l2a                                                      \n",
       "00475310         [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "00475311         [-1.19, -1.15, -1.12, -1.08, -1.04, -1.04, -1....   \n",
       "00475312         [-0.97, -0.97, -0.93, -0.89, -0.85, -0.85, -0....   \n",
       "00475313         [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "00475314         [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "                                                             rh_a2  \\\n",
       "shot_number_l2a                                                      \n",
       "00475310         [-1.08, -1.04, -1.0, -0.97, -0.93, -0.89, -0.8...   \n",
       "00475311         [-2.35, -2.27, -2.16, -2.09, -2.01, -1.94, -1....   \n",
       "00475312         [-2.46, -2.39, -2.31, -2.24, -2.16, -2.09, -2....   \n",
       "00475313         [-1.34, -1.3, -1.26, -1.23, -1.19, -1.15, -1.1...   \n",
       "00475314         [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "                                                             rh_a3  \\\n",
       "shot_number_l2a                                                      \n",
       "00475310         [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "00475311         [-1.26, -1.23, -1.19, -1.15, -1.15, -1.12, -1....   \n",
       "00475312         [-1.19, -1.19, -1.15, -1.12, -1.08, -1.08, -1....   \n",
       "00475313         [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "00475314         [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "                                                             rh_a4  \\\n",
       "shot_number_l2a                                                      \n",
       "00475310         [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "00475311         [-1.19, -1.19, -1.15, -1.12, -1.08, -1.08, -1....   \n",
       "00475312         [-0.97, -0.97, -0.93, -0.93, -0.89, -0.89, -0....   \n",
       "00475313         [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "00475314         [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "                                                             rh_a5  \\\n",
       "shot_number_l2a                                                      \n",
       "00475310         [-1.75, -1.68, -1.6, -1.53, -1.45, -1.38, -1.3...   \n",
       "00475311         [-2.87, -2.68, -2.53, -2.39, -2.27, -2.16, -2....   \n",
       "00475312         [-3.06, -2.95, -2.8, -2.68, -2.57, -2.46, -2.3...   \n",
       "00475313         [-0.33, -0.29, -0.22, -0.18, -0.11, -0.03, 0.0...   \n",
       "00475314         [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "                                                             rh_a6  \\\n",
       "shot_number_l2a                                                      \n",
       "00475310         [-0.33, -0.33, -0.29, -0.29, -0.26, -0.22, -0....   \n",
       "00475311         [-1.97, -1.94, -1.86, -1.79, -1.75, -1.68, -1....   \n",
       "00475312         [-2.01, -1.97, -1.9, -1.86, -1.83, -1.75, -1.7...   \n",
       "00475313         [-0.18, -0.18, -0.18, -0.14, -0.14, -0.14, -0....   \n",
       "00475314         [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "                                                            rh_a10  \\\n",
       "shot_number_l2a                                                      \n",
       "00475310         [-4.177726745605469, -4.1077267456054685, -4.0...   \n",
       "00475311         [-2.87, -2.68, -2.53, -2.39, -2.27, -2.16, -2....   \n",
       "00475312         [-3.06, -2.95, -2.8, -2.68, -2.57, -2.46, -2.3...   \n",
       "00475313         [-3.1685696411132813, -3.1285696411132813, -3....   \n",
       "00475314         [158.7861328125, 158.7861328125, 158.786132812...   \n",
       "\n",
       "                 selected_algorithm_l2a  \n",
       "shot_number_l2a                          \n",
       "00475310                              2  \n",
       "00475311                              1  \n",
       "00475312                              1  \n",
       "00475313                              2  \n",
       "00475314                              1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read GEDI L2a file\n",
    "hf_l2a = h5py.File(l2af, 'r')\n",
    "\n",
    "shot_number_l2a = hf_l2a[beam]['shot_number'][:]\n",
    "selected_algorithm_l2a = hf_l2a[beam]['selected_algorithm'][:]\n",
    "\n",
    "# selected_algorithm for a shot may be different for L2A and L4A\n",
    "# it is always advisable to get the RH metrics directly for each \n",
    "# algorithm setting groups.\n",
    "\n",
    "rh_a1 = hf_l2a[beam]['geolocation']['rh_a1'][:]/100\n",
    "rh_a2 = hf_l2a[beam]['geolocation']['rh_a2'][:]/100\n",
    "rh_a3 = hf_l2a[beam]['geolocation']['rh_a3'][:]/100\n",
    "rh_a4 = hf_l2a[beam]['geolocation']['rh_a4'][:]/100\n",
    "rh_a5 = hf_l2a[beam]['geolocation']['rh_a5'][:]/100\n",
    "rh_a6 = hf_l2a[beam]['geolocation']['rh_a6'][:]/100\n",
    "\n",
    "# Algorithm setting group 10 indicates that agorithm setting 5 \n",
    "# has been used but that the lowest detected mode in this\n",
    "# algorithm is likely a noise detection and that a higher mode \n",
    "# has been subsequently used to calculate the metrics. \n",
    "# Please refer to Level 2A user guide for details\n",
    "# https://lpdaac.usgs.gov/documents/998/GEDI02_UserGuide_V21.pdf\n",
    "selected_mode = hf_l4a[beam]['selected_mode'][:] # using L4A selected_mode\n",
    "elevs_allmodes_a5 = hf_l2a[beam]['geolocation']['elevs_allmodes_a5'][:]\n",
    "elev_lowestmode_a5 = hf_l2a[beam]['geolocation']['elev_lowestmode_a5'][:]\n",
    "elev_lowestmode =[]\n",
    "for i in range(selected_mode.shape[0]):\n",
    "    elev_lowestmode.append(elevs_allmodes_a5[i, selected_mode[i]])\n",
    "elev_lowestmode = np.array(elev_lowestmode)\n",
    "rh_a10 = rh_a5 - (elev_lowestmode[:, None] - elev_lowestmode_a5[:, None])\n",
    "\n",
    "df_l2a = pd.DataFrame(list(zip(shot_number_l2a, rh_a1, rh_a2, rh_a3, rh_a4, rh_a5, rh_a6, rh_a10, \n",
    "                               selected_algorithm_l2a)), \n",
    "                     columns=['shot_number_l2a', 'rh_a1', 'rh_a2', 'rh_a3', 'rh_a4', 'rh_a5', 'rh_a6', 'rh_a10', \n",
    "                              'selected_algorithm_l2a'])\n",
    "df_l2a['shot_number_l2a'] = df_l2a['shot_number_l2a'].astype(str).str[-8:]\n",
    "df_l2a.set_index('shot_number_l2a', inplace=True)\n",
    "df_l2a.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ae1a3ca",
   "metadata": {},
   "source": [
    "We will now join the L4A dataframe (`df_l4a`) with the L2A dataframe (`df_2a`) using [pandas' join](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.join.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5e410600",
   "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>agbd_l4a</th>\n",
       "      <th>selected_algorithm_l4a</th>\n",
       "      <th>predict_stratum</th>\n",
       "      <th>rh_a1</th>\n",
       "      <th>rh_a2</th>\n",
       "      <th>rh_a3</th>\n",
       "      <th>rh_a4</th>\n",
       "      <th>rh_a5</th>\n",
       "      <th>rh_a6</th>\n",
       "      <th>rh_a10</th>\n",
       "      <th>selected_algorithm_l2a</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shot_number_l4a</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>00475311</th>\n",
       "      <td>25.602335</td>\n",
       "      <td>1</td>\n",
       "      <td>ENT_Eu</td>\n",
       "      <td>[-1.19, -1.15, -1.12, -1.08, -1.04, -1.04, -1....</td>\n",
       "      <td>[-2.35, -2.27, -2.16, -2.09, -2.01, -1.94, -1....</td>\n",
       "      <td>[-1.26, -1.23, -1.19, -1.15, -1.15, -1.12, -1....</td>\n",
       "      <td>[-1.19, -1.19, -1.15, -1.12, -1.08, -1.08, -1....</td>\n",
       "      <td>[-2.87, -2.68, -2.53, -2.39, -2.27, -2.16, -2....</td>\n",
       "      <td>[-1.97, -1.94, -1.86, -1.79, -1.75, -1.68, -1....</td>\n",
       "      <td>[-2.87, -2.68, -2.53, -2.39, -2.27, -2.16, -2....</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00475312</th>\n",
       "      <td>24.043011</td>\n",
       "      <td>1</td>\n",
       "      <td>ENT_Eu</td>\n",
       "      <td>[-0.97, -0.97, -0.93, -0.89, -0.85, -0.85, -0....</td>\n",
       "      <td>[-2.46, -2.39, -2.31, -2.24, -2.16, -2.09, -2....</td>\n",
       "      <td>[-1.19, -1.19, -1.15, -1.12, -1.08, -1.08, -1....</td>\n",
       "      <td>[-0.97, -0.97, -0.93, -0.93, -0.89, -0.89, -0....</td>\n",
       "      <td>[-3.06, -2.95, -2.8, -2.68, -2.57, -2.46, -2.3...</td>\n",
       "      <td>[-2.01, -1.97, -1.9, -1.86, -1.83, -1.75, -1.7...</td>\n",
       "      <td>[-3.06, -2.95, -2.8, -2.68, -2.57, -2.46, -2.3...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00475317</th>\n",
       "      <td>24.283388</td>\n",
       "      <td>1</td>\n",
       "      <td>ENT_Eu</td>\n",
       "      <td>[-1.94, -1.9, -1.83, -1.79, -1.75, -1.68, -1.6...</td>\n",
       "      <td>[-3.13, -3.02, -2.87, -2.76, -2.65, -2.57, -2....</td>\n",
       "      <td>[-2.24, -2.2, -2.12, -2.09, -2.01, -1.97, -1.9...</td>\n",
       "      <td>[-1.94, -1.9, -1.86, -1.79, -1.75, -1.71, -1.6...</td>\n",
       "      <td>[-3.54, -3.36, -3.17, -3.02, -2.87, -2.76, -2....</td>\n",
       "      <td>[-2.8, -2.72, -2.61, -2.53, -2.46, -2.35, -2.3...</td>\n",
       "      <td>[-3.54, -3.36, -3.17, -3.02, -2.87, -2.76, -2....</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00475318</th>\n",
       "      <td>23.047647</td>\n",
       "      <td>2</td>\n",
       "      <td>ENT_Eu</td>\n",
       "      <td>[-0.14, -0.14, -0.14, -0.11, -0.11, -0.07, -0....</td>\n",
       "      <td>[-2.2, -2.16, -2.09, -2.01, -1.97, -1.9, -1.86...</td>\n",
       "      <td>[-0.07, -0.07, -0.07, -0.03, -0.03, 0.0, 0.0, ...</td>\n",
       "      <td>[-0.14, -0.14, -0.14, -0.14, -0.14, -0.14, -0....</td>\n",
       "      <td>[-3.06, -2.95, -2.83, -2.72, -2.61, -2.5, -2.4...</td>\n",
       "      <td>[-1.56, -1.53, -1.49, -1.45, -1.38, -1.34, -1....</td>\n",
       "      <td>[-3.06, -2.95, -2.83, -2.72, -2.61, -2.5, -2.4...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00475320</th>\n",
       "      <td>23.937260</td>\n",
       "      <td>1</td>\n",
       "      <td>ENT_Eu</td>\n",
       "      <td>[-0.59, -0.59, -0.56, -0.56, -0.52, -0.52, -0....</td>\n",
       "      <td>[-2.65, -2.57, -2.53, -2.46, -2.39, -2.31, -2....</td>\n",
       "      <td>[-0.85, -0.85, -0.82, -0.82, -0.78, -0.78, -0....</td>\n",
       "      <td>[-0.59, -0.59, -0.59, -0.59, -0.56, -0.56, -0....</td>\n",
       "      <td>[-3.36, -3.24, -3.13, -3.06, -2.95, -2.87, -2....</td>\n",
       "      <td>[-2.05, -2.01, -1.97, -1.9, -1.86, -1.83, -1.7...</td>\n",
       "      <td>[-3.36, -3.24, -3.13, -3.06, -2.95, -2.87, -2....</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  agbd_l4a  selected_algorithm_l4a predict_stratum  \\\n",
       "shot_number_l4a                                                      \n",
       "00475311         25.602335                       1          ENT_Eu   \n",
       "00475312         24.043011                       1          ENT_Eu   \n",
       "00475317         24.283388                       1          ENT_Eu   \n",
       "00475318         23.047647                       2          ENT_Eu   \n",
       "00475320         23.937260                       1          ENT_Eu   \n",
       "\n",
       "                                                             rh_a1  \\\n",
       "shot_number_l4a                                                      \n",
       "00475311         [-1.19, -1.15, -1.12, -1.08, -1.04, -1.04, -1....   \n",
       "00475312         [-0.97, -0.97, -0.93, -0.89, -0.85, -0.85, -0....   \n",
       "00475317         [-1.94, -1.9, -1.83, -1.79, -1.75, -1.68, -1.6...   \n",
       "00475318         [-0.14, -0.14, -0.14, -0.11, -0.11, -0.07, -0....   \n",
       "00475320         [-0.59, -0.59, -0.56, -0.56, -0.52, -0.52, -0....   \n",
       "\n",
       "                                                             rh_a2  \\\n",
       "shot_number_l4a                                                      \n",
       "00475311         [-2.35, -2.27, -2.16, -2.09, -2.01, -1.94, -1....   \n",
       "00475312         [-2.46, -2.39, -2.31, -2.24, -2.16, -2.09, -2....   \n",
       "00475317         [-3.13, -3.02, -2.87, -2.76, -2.65, -2.57, -2....   \n",
       "00475318         [-2.2, -2.16, -2.09, -2.01, -1.97, -1.9, -1.86...   \n",
       "00475320         [-2.65, -2.57, -2.53, -2.46, -2.39, -2.31, -2....   \n",
       "\n",
       "                                                             rh_a3  \\\n",
       "shot_number_l4a                                                      \n",
       "00475311         [-1.26, -1.23, -1.19, -1.15, -1.15, -1.12, -1....   \n",
       "00475312         [-1.19, -1.19, -1.15, -1.12, -1.08, -1.08, -1....   \n",
       "00475317         [-2.24, -2.2, -2.12, -2.09, -2.01, -1.97, -1.9...   \n",
       "00475318         [-0.07, -0.07, -0.07, -0.03, -0.03, 0.0, 0.0, ...   \n",
       "00475320         [-0.85, -0.85, -0.82, -0.82, -0.78, -0.78, -0....   \n",
       "\n",
       "                                                             rh_a4  \\\n",
       "shot_number_l4a                                                      \n",
       "00475311         [-1.19, -1.19, -1.15, -1.12, -1.08, -1.08, -1....   \n",
       "00475312         [-0.97, -0.97, -0.93, -0.93, -0.89, -0.89, -0....   \n",
       "00475317         [-1.94, -1.9, -1.86, -1.79, -1.75, -1.71, -1.6...   \n",
       "00475318         [-0.14, -0.14, -0.14, -0.14, -0.14, -0.14, -0....   \n",
       "00475320         [-0.59, -0.59, -0.59, -0.59, -0.56, -0.56, -0....   \n",
       "\n",
       "                                                             rh_a5  \\\n",
       "shot_number_l4a                                                      \n",
       "00475311         [-2.87, -2.68, -2.53, -2.39, -2.27, -2.16, -2....   \n",
       "00475312         [-3.06, -2.95, -2.8, -2.68, -2.57, -2.46, -2.3...   \n",
       "00475317         [-3.54, -3.36, -3.17, -3.02, -2.87, -2.76, -2....   \n",
       "00475318         [-3.06, -2.95, -2.83, -2.72, -2.61, -2.5, -2.4...   \n",
       "00475320         [-3.36, -3.24, -3.13, -3.06, -2.95, -2.87, -2....   \n",
       "\n",
       "                                                             rh_a6  \\\n",
       "shot_number_l4a                                                      \n",
       "00475311         [-1.97, -1.94, -1.86, -1.79, -1.75, -1.68, -1....   \n",
       "00475312         [-2.01, -1.97, -1.9, -1.86, -1.83, -1.75, -1.7...   \n",
       "00475317         [-2.8, -2.72, -2.61, -2.53, -2.46, -2.35, -2.3...   \n",
       "00475318         [-1.56, -1.53, -1.49, -1.45, -1.38, -1.34, -1....   \n",
       "00475320         [-2.05, -2.01, -1.97, -1.9, -1.86, -1.83, -1.7...   \n",
       "\n",
       "                                                            rh_a10  \\\n",
       "shot_number_l4a                                                      \n",
       "00475311         [-2.87, -2.68, -2.53, -2.39, -2.27, -2.16, -2....   \n",
       "00475312         [-3.06, -2.95, -2.8, -2.68, -2.57, -2.46, -2.3...   \n",
       "00475317         [-3.54, -3.36, -3.17, -3.02, -2.87, -2.76, -2....   \n",
       "00475318         [-3.06, -2.95, -2.83, -2.72, -2.61, -2.5, -2.4...   \n",
       "00475320         [-3.36, -3.24, -3.13, -3.06, -2.95, -2.87, -2....   \n",
       "\n",
       "                 selected_algorithm_l2a  \n",
       "shot_number_l4a                          \n",
       "00475311                              1  \n",
       "00475312                              1  \n",
       "00475317                              1  \n",
       "00475318                              2  \n",
       "00475320                              1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_l4a = df_l4a.join(df_l2a)\n",
    "df_l4a = df_l4a[df_l4a.agbd_l4a != -9999] # removing Nan L4A agbd values\n",
    "df_l4a.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de4b702b",
   "metadata": {},
   "source": [
    "Let's calculate AGBD values from L2A RH metrics and save them to a new column 'agbd_l2a' in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3b0757e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_agbd(ps, sag, rh1, rh2, rh3, rh4, rh5, rh6, rh10):\n",
    "    shot_model = df_l4a_model.loc[ps]\n",
    "    agbd_t_l2a = shot_model['par'][0] # intercept\n",
    "    for i in range(shot_model['npar']-1):\n",
    "        r = shot_model['rh_index'][i]\n",
    "        if (sag == 1):\n",
    "            rh = rh1\n",
    "        elif (sag == 2):\n",
    "            rh = rh2\n",
    "        elif (sag == 3):\n",
    "            rh = rh3\n",
    "        elif (sag == 4):\n",
    "            rh = rh4\n",
    "        elif (sag == 5):\n",
    "            rh = rh5\n",
    "        elif (sag == 6):\n",
    "            rh = rh6\n",
    "        elif (sag == 10):\n",
    "            rh = rh10\n",
    "            \n",
    "        xvar_x = np.sqrt(rh[r] + predictor_offset)\n",
    "        agbd_t_l2a += shot_model['par'][i+1] * xvar_x\n",
    "\n",
    "    return (agbd_t_l2a**2) * shot_model['bias_correction_value']\n",
    "\n",
    "predictor_offset = hf_l4a[beam]['agbd_prediction'].attrs['predictor_offset']\n",
    "df_l4a['agbd_l2a']= df_l4a.apply(lambda x: calculate_agbd(x['predict_stratum'], x['selected_algorithm_l4a'],\n",
    "                                                          x['rh_a1'], x['rh_a2'], x['rh_a3'], x['rh_a4'],\n",
    "                                                          x['rh_a5'], x['rh_a6'], x['rh_a10'] ), axis=1)\n",
    "\n",
    "# closing files\n",
    "hf_l4a.close()\n",
    "hf_l2a.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67f17d31",
   "metadata": {},
   "source": [
    "We will plot the L4A AGBD values against the AGBD derived from L2A RH metrics by the algorithm setting groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a274dccd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 812.85x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(data=df_l4a, x=\"agbd_l4a\", y=\"agbd_l2a\", hue=\"predict_stratum\", \n",
    "                col=\"selected_algorithm_l4a\", col_wrap=2,  palette=\"tab20\", facet_kws={'sharey': False, 'sharex': False})\n",
    "sns.despine(top=False, right=False, left=False, bottom=False, offset=None, trim=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e7a9cc2",
   "metadata": {},
   "source": [
    "We will plot the L4A AGBD values against the AGBD derived from L2A RH metrics by the prediction strata to check if the AGBD values look all right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c7549af8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1212.05x1440 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(data=df_l4a, x=\"agbd_l4a\", y=\"agbd_l2a\", hue=\"selected_algorithm_l4a\", \n",
    "                col=\"predict_stratum\", col_wrap=3, palette=\"tab20\", facet_kws={'sharey': False, 'sharex': False})\n",
    "sns.despine(top=False, right=False, left=False, bottom=False, offset=None, trim=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}