notebook/GEDI2_A.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LEVEL 2 - GEDI Level 2A Geolocated Elevation and Height Metrics product\n",
    "\n",
    "https://lpdaac.usgs.gov/products/gedi02_av001/  \n",
    "The purpose of the GEDI Level 2A Geolocated Elevation and Height Metrics product (GEDI02_A) is to provide waveform interpretation and extracted products from each GEDI01_B received waveform, including ground elevation, canopy top height, and relative height (RH) metrics. The methodology for generating the GEDI02_A product datasets is adapted from the Land, Vegetation, and Ice Sensor (LVIS) algorithm. The GEDI01_B product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from pyGEDI import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select the file of Product GEDI0A_B with the date of interest. This will be read as an h5 file which will be used to extrapolate ground elevation, canopy top height, and relative height (RH) metrics.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "fileh5_2A='data/GEDI02_A_2019108080338_O01964_T05337_02_001_01_sub.h5'\n",
    "h5_2A=getH5(fileh5_2A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the coordinates of your specific area of interest. The next line will exhibit an example using coordinates in Brazil."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ul_lat=-13.76913\n",
    "ul_lon=-44.0654\n",
    "lr_lat=-13.67646\n",
    "lr_lon=-44.17246\n",
    "\n",
    "bbox=[ul_lat,ul_lon,lr_lat,lr_lon]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GEDI02_A data product contains 156 layers for each of the eight beams, including ground elevation, canopy top height, relative return energy metrics (describing canopy vertical structure, for example), and many other interpreted products from the return waveforms. Additional information for the layers can be found in the GEDI Level 2A Dictionary. https://lpdaac.usgs.gov/documents/586/gedi_l2a_dictionary_P001_v1.html\n",
    "\n",
    "To visualize the different layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data/GEDI02_A_2019108080338_O01964_T05337_02_001_01_sub.h5': ['ancillary/l2a_alg_count',\n",
       "  'beam',\n",
       "  'channel',\n",
       "  'degrade_flag',\n",
       "  'delta_time',\n",
       "  'digital_elevation_model',\n",
       "  'elev_highestreturn',\n",
       "  'elev_lowestmode',\n",
       "  'elevation_bias_flag',\n",
       "  'elevation_bin0_error',\n",
       "  'energy_total',\n",
       "  'geolocation/elev_highestreturn_a1',\n",
       "  'geolocation/elev_highestreturn_a2',\n",
       "  'geolocation/elev_highestreturn_a3',\n",
       "  'geolocation/elev_highestreturn_a4',\n",
       "  'geolocation/elev_highestreturn_a5',\n",
       "  'geolocation/elev_highestreturn_a6',\n",
       "  'geolocation/elev_lowestmode_a1',\n",
       "  'geolocation/elev_lowestmode_a2',\n",
       "  'geolocation/elev_lowestmode_a3',\n",
       "  'geolocation/elev_lowestmode_a4',\n",
       "  'geolocation/elev_lowestmode_a5',\n",
       "  'geolocation/elev_lowestmode_a6',\n",
       "  'geolocation/elev_lowestreturn_a1',\n",
       "  'geolocation/elev_lowestreturn_a2',\n",
       "  'geolocation/elev_lowestreturn_a3',\n",
       "  'geolocation/elev_lowestreturn_a4',\n",
       "  'geolocation/elev_lowestreturn_a5',\n",
       "  'geolocation/elev_lowestreturn_a6',\n",
       "  'geolocation/elevation_1gfit',\n",
       "  'geolocation/elevs_allmodes_a1',\n",
       "  'geolocation/elevs_allmodes_a2',\n",
       "  'geolocation/elevs_allmodes_a3',\n",
       "  'geolocation/elevs_allmodes_a4',\n",
       "  'geolocation/elevs_allmodes_a5',\n",
       "  'geolocation/elevs_allmodes_a6',\n",
       "  'geolocation/energy_lowestmode_a1',\n",
       "  'geolocation/energy_lowestmode_a2',\n",
       "  'geolocation/energy_lowestmode_a3',\n",
       "  'geolocation/energy_lowestmode_a4',\n",
       "  'geolocation/energy_lowestmode_a5',\n",
       "  'geolocation/energy_lowestmode_a6',\n",
       "  'geolocation/lat_highestreturn_a1',\n",
       "  'geolocation/lat_highestreturn_a2',\n",
       "  'geolocation/lat_highestreturn_a3',\n",
       "  'geolocation/lat_highestreturn_a4',\n",
       "  'geolocation/lat_highestreturn_a5',\n",
       "  'geolocation/lat_highestreturn_a6',\n",
       "  'geolocation/lat_lowestmode_a1',\n",
       "  'geolocation/lat_lowestmode_a2',\n",
       "  'geolocation/lat_lowestmode_a3',\n",
       "  'geolocation/lat_lowestmode_a4',\n",
       "  'geolocation/lat_lowestmode_a5',\n",
       "  'geolocation/lat_lowestmode_a6',\n",
       "  'geolocation/lat_lowestreturn_a1',\n",
       "  'geolocation/lat_lowestreturn_a2',\n",
       "  'geolocation/lat_lowestreturn_a3',\n",
       "  'geolocation/lat_lowestreturn_a4',\n",
       "  'geolocation/lat_lowestreturn_a5',\n",
       "  'geolocation/lat_lowestreturn_a6',\n",
       "  'geolocation/latitude_1gfit',\n",
       "  'geolocation/lats_allmodes_a1',\n",
       "  'geolocation/lats_allmodes_a2',\n",
       "  'geolocation/lats_allmodes_a3',\n",
       "  'geolocation/lats_allmodes_a4',\n",
       "  'geolocation/lats_allmodes_a5',\n",
       "  'geolocation/lats_allmodes_a6',\n",
       "  'geolocation/lon_highestreturn_a1',\n",
       "  'geolocation/lon_highestreturn_a2',\n",
       "  'geolocation/lon_highestreturn_a3',\n",
       "  'geolocation/lon_highestreturn_a4',\n",
       "  'geolocation/lon_highestreturn_a5',\n",
       "  'geolocation/lon_highestreturn_a6',\n",
       "  'geolocation/lon_lowestmode_a1',\n",
       "  'geolocation/lon_lowestmode_a2',\n",
       "  'geolocation/lon_lowestmode_a3',\n",
       "  'geolocation/lon_lowestmode_a4',\n",
       "  'geolocation/lon_lowestmode_a5',\n",
       "  'geolocation/lon_lowestmode_a6',\n",
       "  'geolocation/lon_lowestreturn_a1',\n",
       "  'geolocation/lon_lowestreturn_a2',\n",
       "  'geolocation/lon_lowestreturn_a3',\n",
       "  'geolocation/lon_lowestreturn_a4',\n",
       "  'geolocation/lon_lowestreturn_a5',\n",
       "  'geolocation/lon_lowestreturn_a6',\n",
       "  'geolocation/longitude_1gfit',\n",
       "  'geolocation/lons_allmodes_a1',\n",
       "  'geolocation/lons_allmodes_a2',\n",
       "  'geolocation/lons_allmodes_a3',\n",
       "  'geolocation/lons_allmodes_a4',\n",
       "  'geolocation/lons_allmodes_a5',\n",
       "  'geolocation/lons_allmodes_a6',\n",
       "  'geolocation/num_detectedmodes_a1',\n",
       "  'geolocation/num_detectedmodes_a2',\n",
       "  'geolocation/num_detectedmodes_a3',\n",
       "  'geolocation/num_detectedmodes_a4',\n",
       "  'geolocation/num_detectedmodes_a5',\n",
       "  'geolocation/num_detectedmodes_a6',\n",
       "  'geolocation/quality_flag_a1',\n",
       "  'geolocation/quality_flag_a2',\n",
       "  'geolocation/quality_flag_a3',\n",
       "  'geolocation/quality_flag_a4',\n",
       "  'geolocation/quality_flag_a5',\n",
       "  'geolocation/quality_flag_a6',\n",
       "  'geolocation/rh_a1',\n",
       "  'geolocation/rh_a2',\n",
       "  'geolocation/rh_a3',\n",
       "  'geolocation/rh_a4',\n",
       "  'geolocation/rh_a5',\n",
       "  'geolocation/rh_a6',\n",
       "  'geolocation/sensitivity_a1',\n",
       "  'geolocation/sensitivity_a2',\n",
       "  'geolocation/sensitivity_a3',\n",
       "  'geolocation/sensitivity_a4',\n",
       "  'geolocation/sensitivity_a5',\n",
       "  'geolocation/sensitivity_a6',\n",
       "  'geolocation/shot_number',\n",
       "  'geolocation/stale_return_flag',\n",
       "  'land_cover_data/landsat_treecover',\n",
       "  'land_cover_data/modis_nonvegetated',\n",
       "  'land_cover_data/modis_nonvegetated_sd',\n",
       "  'land_cover_data/modis_treecover',\n",
       "  'land_cover_data/modis_treecover_sd',\n",
       "  'lat_highestreturn',\n",
       "  'lat_lowestmode',\n",
       "  'latitude_bin0_error',\n",
       "  'lon_highestreturn',\n",
       "  'lon_lowestmode',\n",
       "  'longitude_bin0_error',\n",
       "  'master_frac',\n",
       "  'master_int',\n",
       "  'mean_sea_surface',\n",
       "  'num_detectedmodes',\n",
       "  'quality_flag',\n",
       "  'rh',\n",
       "  'rx_1gaussfit/ancillary/mpfit_max_func_evals',\n",
       "  'rx_1gaussfit/ancillary/mpfit_maxiters',\n",
       "  'rx_1gaussfit/ancillary/mpfit_tolerance',\n",
       "  'rx_1gaussfit/ancillary/rx_constraint_gamplitude_lower',\n",
       "  'rx_1gaussfit/ancillary/rx_constraint_gamplitude_upper',\n",
       "  'rx_1gaussfit/ancillary/rx_constraint_gloc_lower',\n",
       "  'rx_1gaussfit/ancillary/rx_constraint_gloc_upper',\n",
       "  'rx_1gaussfit/ancillary/rx_constraint_gwidth_lower',\n",
       "  'rx_1gaussfit/ancillary/rx_constraint_gwidth_upper',\n",
       "  'rx_1gaussfit/ancillary/rx_estimate_bias',\n",
       "  'rx_1gaussfit/ancillary/rx_mean_noise_level',\n",
       "  'rx_1gaussfit/ancillary/rx_smoothwidth',\n",
       "  'rx_1gaussfit/rx_gamplitude',\n",
       "  'rx_1gaussfit/rx_gamplitude_error',\n",
       "  'rx_1gaussfit/rx_gbias',\n",
       "  'rx_1gaussfit/rx_gbias_error',\n",
       "  'rx_1gaussfit/rx_gchisq',\n",
       "  'rx_1gaussfit/rx_gflag',\n",
       "  'rx_1gaussfit/rx_giters',\n",
       "  'rx_1gaussfit/rx_gloc',\n",
       "  'rx_1gaussfit/rx_gloc_error',\n",
       "  'rx_1gaussfit/rx_gwidth',\n",
       "  'rx_1gaussfit/rx_gwidth_error',\n",
       "  'rx_assess/ancillary/rx_ampbounds_ll',\n",
       "  'rx_assess/ancillary/rx_ampbounds_ul',\n",
       "  'rx_assess/ancillary/rx_clipamp',\n",
       "  'rx_assess/ancillary/rx_pulsethresh',\n",
       "  'rx_assess/ancillary/rx_ringthresh',\n",
       "  'rx_assess/ancillary/smoothing_width_locs',\n",
       "  'rx_assess/mean',\n",
       "  'rx_assess/mean_64kadjusted',\n",
       "  'rx_assess/ocean_calibration_shot_flag',\n",
       "  'rx_assess/quality_flag',\n",
       "  'rx_assess/rx_assess_flag',\n",
       "  'rx_assess/rx_clipbin0',\n",
       "  'rx_assess/rx_clipbin_count',\n",
       "  'rx_assess/rx_energy',\n",
       "  'rx_assess/rx_maxamp',\n",
       "  'rx_assess/rx_maxpeakloc',\n",
       "  'rx_assess/sd_corrected',\n",
       "  'rx_assess/shot_number',\n",
       "  'rx_processing_a1/ancillary/amp_thresh',\n",
       "  'rx_processing_a1/ancillary/ampval_limit2',\n",
       "  'rx_processing_a1/ancillary/ampval_limit3',\n",
       "  'rx_processing_a1/ancillary/botlocdist_limit1',\n",
       "  'rx_processing_a1/ancillary/botlocdist_limit2',\n",
       "  'rx_processing_a1/ancillary/botlocdist_limit3',\n",
       "  'rx_processing_a1/ancillary/cumulative_energy_minimum',\n",
       "  'rx_processing_a1/ancillary/cumulative_energy_thresh',\n",
       "  'rx_processing_a1/ancillary/enable_select_mode',\n",
       "  'rx_processing_a1/ancillary/energy_thresh',\n",
       "  'rx_processing_a1/ancillary/preprocessor_threshold',\n",
       "  'rx_processing_a1/ancillary/pulse_sep_thresh',\n",
       "  'rx_processing_a1/ancillary/rx_back_threshold',\n",
       "  'rx_processing_a1/ancillary/rx_front_threshold',\n",
       "  'rx_processing_a1/ancillary/rx_max_mode_count',\n",
       "  'rx_processing_a1/ancillary/rx_searchsize',\n",
       "  'rx_processing_a1/ancillary/rx_sentinel_location',\n",
       "  'rx_processing_a1/ancillary/rx_smoothing_width_locs',\n",
       "  'rx_processing_a1/ancillary/rx_smoothing_width_zcross',\n",
       "  'rx_processing_a1/ancillary/rx_subbin_resolution',\n",
       "  'rx_processing_a1/ancillary/rx_use_fixed_thresholds',\n",
       "  'rx_processing_a1/back_threshold',\n",
       "  'rx_processing_a1/botloc',\n",
       "  'rx_processing_a1/botloc_amp',\n",
       "  'rx_processing_a1/energy_sm',\n",
       "  'rx_processing_a1/front_threshold',\n",
       "  'rx_processing_a1/lastmodeenergy',\n",
       "  'rx_processing_a1/mean',\n",
       "  'rx_processing_a1/mean_sm',\n",
       "  'rx_processing_a1/min_detection_energy',\n",
       "  'rx_processing_a1/min_detection_threshold',\n",
       "  'rx_processing_a1/peak',\n",
       "  'rx_processing_a1/pk_sm',\n",
       "  'rx_processing_a1/rx_algrunflag',\n",
       "  'rx_processing_a1/rx_cumulative',\n",
       "  'rx_processing_a1/rx_iwaveamps',\n",
       "  'rx_processing_a1/rx_modeamps',\n",
       "  'rx_processing_a1/rx_modeenergytobotloc',\n",
       "  'rx_processing_a1/rx_modelocalenergy',\n",
       "  'rx_processing_a1/rx_modelocalenergyabovemean',\n",
       "  'rx_processing_a1/rx_modelocalslope',\n",
       "  'rx_processing_a1/rx_modelocs',\n",
       "  'rx_processing_a1/rx_modewidths',\n",
       "  'rx_processing_a1/rx_nummodes',\n",
       "  'rx_processing_a1/sd_sm',\n",
       "  'rx_processing_a1/search_end',\n",
       "  'rx_processing_a1/search_start',\n",
       "  'rx_processing_a1/selected_mode',\n",
       "  'rx_processing_a1/selected_mode_flag',\n",
       "  'rx_processing_a1/shot_number',\n",
       "  'rx_processing_a1/smoothwidth',\n",
       "  'rx_processing_a1/smoothwidth_zcross',\n",
       "  'rx_processing_a1/stddev',\n",
       "  'rx_processing_a1/toploc',\n",
       "  'rx_processing_a1/toploc_miss',\n",
       "  'rx_processing_a1/zcross',\n",
       "  'rx_processing_a1/zcross0',\n",
       "  'rx_processing_a1/zcross_amp',\n",
       "  'rx_processing_a1/zcross_localenergy',\n",
       "  'rx_processing_a2/ancillary/amp_thresh',\n",
       "  'rx_processing_a2/ancillary/ampval_limit2',\n",
       "  'rx_processing_a2/ancillary/ampval_limit3',\n",
       "  'rx_processing_a2/ancillary/botlocdist_limit1',\n",
       "  'rx_processing_a2/ancillary/botlocdist_limit2',\n",
       "  'rx_processing_a2/ancillary/botlocdist_limit3',\n",
       "  'rx_processing_a2/ancillary/cumulative_energy_minimum',\n",
       "  'rx_processing_a2/ancillary/cumulative_energy_thresh',\n",
       "  'rx_processing_a2/ancillary/enable_select_mode',\n",
       "  'rx_processing_a2/ancillary/energy_thresh',\n",
       "  'rx_processing_a2/ancillary/preprocessor_threshold',\n",
       "  'rx_processing_a2/ancillary/pulse_sep_thresh',\n",
       "  'rx_processing_a2/ancillary/rx_back_threshold',\n",
       "  'rx_processing_a2/ancillary/rx_front_threshold',\n",
       "  'rx_processing_a2/ancillary/rx_max_mode_count',\n",
       "  'rx_processing_a2/ancillary/rx_searchsize',\n",
       "  'rx_processing_a2/ancillary/rx_sentinel_location',\n",
       "  'rx_processing_a2/ancillary/rx_smoothing_width_locs',\n",
       "  'rx_processing_a2/ancillary/rx_smoothing_width_zcross',\n",
       "  'rx_processing_a2/ancillary/rx_subbin_resolution',\n",
       "  'rx_processing_a2/ancillary/rx_use_fixed_thresholds',\n",
       "  'rx_processing_a2/back_threshold',\n",
       "  'rx_processing_a2/botloc',\n",
       "  'rx_processing_a2/botloc_amp',\n",
       "  'rx_processing_a2/energy_sm',\n",
       "  'rx_processing_a2/front_threshold',\n",
       "  'rx_processing_a2/lastmodeenergy',\n",
       "  'rx_processing_a2/mean',\n",
       "  'rx_processing_a2/mean_sm',\n",
       "  'rx_processing_a2/min_detection_energy',\n",
       "  'rx_processing_a2/min_detection_threshold',\n",
       "  'rx_processing_a2/peak',\n",
       "  'rx_processing_a2/pk_sm',\n",
       "  'rx_processing_a2/rx_algrunflag',\n",
       "  'rx_processing_a2/rx_cumulative',\n",
       "  'rx_processing_a2/rx_iwaveamps',\n",
       "  'rx_processing_a2/rx_modeamps',\n",
       "  'rx_processing_a2/rx_modeenergytobotloc',\n",
       "  'rx_processing_a2/rx_modelocalenergy',\n",
       "  'rx_processing_a2/rx_modelocalenergyabovemean',\n",
       "  'rx_processing_a2/rx_modelocalslope',\n",
       "  'rx_processing_a2/rx_modelocs',\n",
       "  'rx_processing_a2/rx_modewidths',\n",
       "  'rx_processing_a2/rx_nummodes',\n",
       "  'rx_processing_a2/sd_sm',\n",
       "  'rx_processing_a2/search_end',\n",
       "  'rx_processing_a2/search_start',\n",
       "  'rx_processing_a2/selected_mode',\n",
       "  'rx_processing_a2/selected_mode_flag',\n",
       "  'rx_processing_a2/shot_number',\n",
       "  'rx_processing_a2/smoothwidth',\n",
       "  'rx_processing_a2/smoothwidth_zcross',\n",
       "  'rx_processing_a2/stddev',\n",
       "  'rx_processing_a2/toploc',\n",
       "  'rx_processing_a2/toploc_miss',\n",
       "  'rx_processing_a2/zcross',\n",
       "  'rx_processing_a2/zcross0',\n",
       "  'rx_processing_a2/zcross_amp',\n",
       "  'rx_processing_a2/zcross_localenergy',\n",
       "  'rx_processing_a3/ancillary/amp_thresh',\n",
       "  'rx_processing_a3/ancillary/ampval_limit2',\n",
       "  'rx_processing_a3/ancillary/ampval_limit3',\n",
       "  'rx_processing_a3/ancillary/botlocdist_limit1',\n",
       "  'rx_processing_a3/ancillary/botlocdist_limit2',\n",
       "  'rx_processing_a3/ancillary/botlocdist_limit3',\n",
       "  'rx_processing_a3/ancillary/cumulative_energy_minimum',\n",
       "  'rx_processing_a3/ancillary/cumulative_energy_thresh',\n",
       "  'rx_processing_a3/ancillary/enable_select_mode',\n",
       "  'rx_processing_a3/ancillary/energy_thresh',\n",
       "  'rx_processing_a3/ancillary/preprocessor_threshold',\n",
       "  'rx_processing_a3/ancillary/pulse_sep_thresh',\n",
       "  'rx_processing_a3/ancillary/rx_back_threshold',\n",
       "  'rx_processing_a3/ancillary/rx_front_threshold',\n",
       "  'rx_processing_a3/ancillary/rx_max_mode_count',\n",
       "  'rx_processing_a3/ancillary/rx_searchsize',\n",
       "  'rx_processing_a3/ancillary/rx_sentinel_location',\n",
       "  'rx_processing_a3/ancillary/rx_smoothing_width_locs',\n",
       "  'rx_processing_a3/ancillary/rx_smoothing_width_zcross',\n",
       "  'rx_processing_a3/ancillary/rx_subbin_resolution',\n",
       "  'rx_processing_a3/ancillary/rx_use_fixed_thresholds',\n",
       "  'rx_processing_a3/back_threshold',\n",
       "  'rx_processing_a3/botloc',\n",
       "  'rx_processing_a3/botloc_amp',\n",
       "  'rx_processing_a3/energy_sm',\n",
       "  'rx_processing_a3/front_threshold',\n",
       "  'rx_processing_a3/lastmodeenergy',\n",
       "  'rx_processing_a3/mean',\n",
       "  'rx_processing_a3/mean_sm',\n",
       "  'rx_processing_a3/min_detection_energy',\n",
       "  'rx_processing_a3/min_detection_threshold',\n",
       "  'rx_processing_a3/peak',\n",
       "  'rx_processing_a3/pk_sm',\n",
       "  'rx_processing_a3/rx_algrunflag',\n",
       "  'rx_processing_a3/rx_cumulative',\n",
       "  'rx_processing_a3/rx_iwaveamps',\n",
       "  'rx_processing_a3/rx_modeamps',\n",
       "  'rx_processing_a3/rx_modeenergytobotloc',\n",
       "  'rx_processing_a3/rx_modelocalenergy',\n",
       "  'rx_processing_a3/rx_modelocalenergyabovemean',\n",
       "  'rx_processing_a3/rx_modelocalslope',\n",
       "  'rx_processing_a3/rx_modelocs',\n",
       "  'rx_processing_a3/rx_modewidths',\n",
       "  'rx_processing_a3/rx_nummodes',\n",
       "  'rx_processing_a3/sd_sm',\n",
       "  'rx_processing_a3/search_end',\n",
       "  'rx_processing_a3/search_start',\n",
       "  'rx_processing_a3/selected_mode',\n",
       "  'rx_processing_a3/selected_mode_flag',\n",
       "  'rx_processing_a3/shot_number',\n",
       "  'rx_processing_a3/smoothwidth',\n",
       "  'rx_processing_a3/smoothwidth_zcross',\n",
       "  'rx_processing_a3/stddev',\n",
       "  'rx_processing_a3/toploc',\n",
       "  'rx_processing_a3/toploc_miss',\n",
       "  'rx_processing_a3/zcross',\n",
       "  'rx_processing_a3/zcross0',\n",
       "  'rx_processing_a3/zcross_amp',\n",
       "  'rx_processing_a3/zcross_localenergy',\n",
       "  'rx_processing_a4/ancillary/amp_thresh',\n",
       "  'rx_processing_a4/ancillary/ampval_limit2',\n",
       "  'rx_processing_a4/ancillary/ampval_limit3',\n",
       "  'rx_processing_a4/ancillary/botlocdist_limit1',\n",
       "  'rx_processing_a4/ancillary/botlocdist_limit2',\n",
       "  'rx_processing_a4/ancillary/botlocdist_limit3',\n",
       "  'rx_processing_a4/ancillary/cumulative_energy_minimum',\n",
       "  'rx_processing_a4/ancillary/cumulative_energy_thresh',\n",
       "  'rx_processing_a4/ancillary/enable_select_mode',\n",
       "  'rx_processing_a4/ancillary/energy_thresh',\n",
       "  'rx_processing_a4/ancillary/preprocessor_threshold',\n",
       "  'rx_processing_a4/ancillary/pulse_sep_thresh',\n",
       "  'rx_processing_a4/ancillary/rx_back_threshold',\n",
       "  'rx_processing_a4/ancillary/rx_front_threshold',\n",
       "  'rx_processing_a4/ancillary/rx_max_mode_count',\n",
       "  'rx_processing_a4/ancillary/rx_searchsize',\n",
       "  'rx_processing_a4/ancillary/rx_sentinel_location',\n",
       "  'rx_processing_a4/ancillary/rx_smoothing_width_locs',\n",
       "  'rx_processing_a4/ancillary/rx_smoothing_width_zcross',\n",
       "  'rx_processing_a4/ancillary/rx_subbin_resolution',\n",
       "  'rx_processing_a4/ancillary/rx_use_fixed_thresholds',\n",
       "  'rx_processing_a4/back_threshold',\n",
       "  'rx_processing_a4/botloc',\n",
       "  'rx_processing_a4/botloc_amp',\n",
       "  'rx_processing_a4/energy_sm',\n",
       "  'rx_processing_a4/front_threshold',\n",
       "  'rx_processing_a4/lastmodeenergy',\n",
       "  'rx_processing_a4/mean',\n",
       "  'rx_processing_a4/mean_sm',\n",
       "  'rx_processing_a4/min_detection_energy',\n",
       "  'rx_processing_a4/min_detection_threshold',\n",
       "  'rx_processing_a4/peak',\n",
       "  'rx_processing_a4/pk_sm',\n",
       "  'rx_processing_a4/rx_algrunflag',\n",
       "  'rx_processing_a4/rx_cumulative',\n",
       "  'rx_processing_a4/rx_iwaveamps',\n",
       "  'rx_processing_a4/rx_modeamps',\n",
       "  'rx_processing_a4/rx_modeenergytobotloc',\n",
       "  'rx_processing_a4/rx_modelocalenergy',\n",
       "  'rx_processing_a4/rx_modelocalenergyabovemean',\n",
       "  'rx_processing_a4/rx_modelocalslope',\n",
       "  'rx_processing_a4/rx_modelocs',\n",
       "  'rx_processing_a4/rx_modewidths',\n",
       "  'rx_processing_a4/rx_nummodes',\n",
       "  'rx_processing_a4/sd_sm',\n",
       "  'rx_processing_a4/search_end',\n",
       "  'rx_processing_a4/search_start',\n",
       "  'rx_processing_a4/selected_mode',\n",
       "  'rx_processing_a4/selected_mode_flag',\n",
       "  'rx_processing_a4/shot_number',\n",
       "  'rx_processing_a4/smoothwidth',\n",
       "  'rx_processing_a4/smoothwidth_zcross',\n",
       "  'rx_processing_a4/stddev',\n",
       "  'rx_processing_a4/toploc',\n",
       "  'rx_processing_a4/toploc_miss',\n",
       "  'rx_processing_a4/zcross',\n",
       "  'rx_processing_a4/zcross0',\n",
       "  'rx_processing_a4/zcross_amp',\n",
       "  'rx_processing_a4/zcross_localenergy',\n",
       "  'rx_processing_a5/ancillary/amp_thresh',\n",
       "  'rx_processing_a5/ancillary/ampval_limit2',\n",
       "  'rx_processing_a5/ancillary/ampval_limit3',\n",
       "  'rx_processing_a5/ancillary/botlocdist_limit1',\n",
       "  'rx_processing_a5/ancillary/botlocdist_limit2',\n",
       "  'rx_processing_a5/ancillary/botlocdist_limit3',\n",
       "  'rx_processing_a5/ancillary/cumulative_energy_minimum',\n",
       "  'rx_processing_a5/ancillary/cumulative_energy_thresh',\n",
       "  'rx_processing_a5/ancillary/enable_select_mode',\n",
       "  'rx_processing_a5/ancillary/energy_thresh',\n",
       "  'rx_processing_a5/ancillary/preprocessor_threshold',\n",
       "  'rx_processing_a5/ancillary/pulse_sep_thresh',\n",
       "  'rx_processing_a5/ancillary/rx_back_threshold',\n",
       "  'rx_processing_a5/ancillary/rx_front_threshold',\n",
       "  'rx_processing_a5/ancillary/rx_max_mode_count',\n",
       "  'rx_processing_a5/ancillary/rx_searchsize',\n",
       "  'rx_processing_a5/ancillary/rx_sentinel_location',\n",
       "  'rx_processing_a5/ancillary/rx_smoothing_width_locs',\n",
       "  'rx_processing_a5/ancillary/rx_smoothing_width_zcross',\n",
       "  'rx_processing_a5/ancillary/rx_subbin_resolution',\n",
       "  'rx_processing_a5/ancillary/rx_use_fixed_thresholds',\n",
       "  'rx_processing_a5/back_threshold',\n",
       "  'rx_processing_a5/botloc',\n",
       "  'rx_processing_a5/botloc_amp',\n",
       "  'rx_processing_a5/energy_sm',\n",
       "  'rx_processing_a5/front_threshold',\n",
       "  'rx_processing_a5/lastmodeenergy',\n",
       "  'rx_processing_a5/mean',\n",
       "  'rx_processing_a5/mean_sm',\n",
       "  'rx_processing_a5/min_detection_energy',\n",
       "  'rx_processing_a5/min_detection_threshold',\n",
       "  'rx_processing_a5/peak',\n",
       "  'rx_processing_a5/pk_sm',\n",
       "  'rx_processing_a5/rx_algrunflag',\n",
       "  'rx_processing_a5/rx_cumulative',\n",
       "  'rx_processing_a5/rx_iwaveamps',\n",
       "  'rx_processing_a5/rx_modeamps',\n",
       "  'rx_processing_a5/rx_modeenergytobotloc',\n",
       "  'rx_processing_a5/rx_modelocalenergy',\n",
       "  'rx_processing_a5/rx_modelocalenergyabovemean',\n",
       "  'rx_processing_a5/rx_modelocalslope',\n",
       "  'rx_processing_a5/rx_modelocs',\n",
       "  'rx_processing_a5/rx_modewidths',\n",
       "  'rx_processing_a5/rx_nummodes',\n",
       "  'rx_processing_a5/sd_sm',\n",
       "  'rx_processing_a5/search_end',\n",
       "  'rx_processing_a5/search_start',\n",
       "  'rx_processing_a5/selected_mode',\n",
       "  'rx_processing_a5/selected_mode_flag',\n",
       "  'rx_processing_a5/shot_number',\n",
       "  'rx_processing_a5/smoothwidth',\n",
       "  'rx_processing_a5/smoothwidth_zcross',\n",
       "  'rx_processing_a5/stddev',\n",
       "  'rx_processing_a5/toploc',\n",
       "  'rx_processing_a5/toploc_miss',\n",
       "  'rx_processing_a5/zcross',\n",
       "  'rx_processing_a5/zcross0',\n",
       "  'rx_processing_a5/zcross_amp',\n",
       "  'rx_processing_a5/zcross_localenergy',\n",
       "  'rx_processing_a6/ancillary/amp_thresh',\n",
       "  'rx_processing_a6/ancillary/ampval_limit2',\n",
       "  'rx_processing_a6/ancillary/ampval_limit3',\n",
       "  'rx_processing_a6/ancillary/botlocdist_limit1',\n",
       "  'rx_processing_a6/ancillary/botlocdist_limit2',\n",
       "  'rx_processing_a6/ancillary/botlocdist_limit3',\n",
       "  'rx_processing_a6/ancillary/cumulative_energy_minimum',\n",
       "  'rx_processing_a6/ancillary/cumulative_energy_thresh',\n",
       "  'rx_processing_a6/ancillary/enable_select_mode',\n",
       "  'rx_processing_a6/ancillary/energy_thresh',\n",
       "  'rx_processing_a6/ancillary/preprocessor_threshold',\n",
       "  'rx_processing_a6/ancillary/pulse_sep_thresh',\n",
       "  'rx_processing_a6/ancillary/rx_back_threshold',\n",
       "  'rx_processing_a6/ancillary/rx_front_threshold',\n",
       "  'rx_processing_a6/ancillary/rx_max_mode_count',\n",
       "  'rx_processing_a6/ancillary/rx_searchsize',\n",
       "  'rx_processing_a6/ancillary/rx_sentinel_location',\n",
       "  'rx_processing_a6/ancillary/rx_smoothing_width_locs',\n",
       "  'rx_processing_a6/ancillary/rx_smoothing_width_zcross',\n",
       "  'rx_processing_a6/ancillary/rx_subbin_resolution',\n",
       "  'rx_processing_a6/ancillary/rx_use_fixed_thresholds',\n",
       "  'rx_processing_a6/back_threshold',\n",
       "  'rx_processing_a6/botloc',\n",
       "  'rx_processing_a6/botloc_amp',\n",
       "  'rx_processing_a6/energy_sm',\n",
       "  'rx_processing_a6/front_threshold',\n",
       "  'rx_processing_a6/lastmodeenergy',\n",
       "  'rx_processing_a6/mean',\n",
       "  'rx_processing_a6/mean_sm',\n",
       "  'rx_processing_a6/min_detection_energy',\n",
       "  'rx_processing_a6/min_detection_threshold',\n",
       "  'rx_processing_a6/peak',\n",
       "  'rx_processing_a6/pk_sm',\n",
       "  'rx_processing_a6/rx_algrunflag',\n",
       "  'rx_processing_a6/rx_cumulative',\n",
       "  'rx_processing_a6/rx_iwaveamps',\n",
       "  'rx_processing_a6/rx_modeamps',\n",
       "  'rx_processing_a6/rx_modeenergytobotloc',\n",
       "  'rx_processing_a6/rx_modelocalenergy',\n",
       "  'rx_processing_a6/rx_modelocalenergyabovemean',\n",
       "  'rx_processing_a6/rx_modelocalslope',\n",
       "  'rx_processing_a6/rx_modelocs',\n",
       "  'rx_processing_a6/rx_modewidths',\n",
       "  'rx_processing_a6/rx_nummodes',\n",
       "  'rx_processing_a6/sd_sm',\n",
       "  'rx_processing_a6/search_end',\n",
       "  'rx_processing_a6/search_start',\n",
       "  'rx_processing_a6/selected_mode',\n",
       "  'rx_processing_a6/selected_mode_flag',\n",
       "  'rx_processing_a6/shot_number',\n",
       "  'rx_processing_a6/smoothwidth',\n",
       "  'rx_processing_a6/smoothwidth_zcross',\n",
       "  'rx_processing_a6/stddev',\n",
       "  'rx_processing_a6/toploc',\n",
       "  'rx_processing_a6/toploc_miss',\n",
       "  'rx_processing_a6/zcross',\n",
       "  'rx_processing_a6/zcross0',\n",
       "  'rx_processing_a6/zcross_amp',\n",
       "  'rx_processing_a6/zcross_localenergy',\n",
       "  'selected_algorithm',\n",
       "  'selected_mode',\n",
       "  'sensitivity',\n",
       "  'shot_number',\n",
       "  'solar_azimuth',\n",
       "  'solar_elevation',\n",
       "  'surface_flag']}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getLayer('',[h5_2A])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see if a specific layer exists or not:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "idsbox=idsBox(h5_2A,'lat_lowestmode','lon_lowestmode',bbox)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**For example:** With the Box defined the different layers can be extracted from product GEDI02_A for the area of interest. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "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>shot_number</th>\n",
       "      <th>lat_lowestmode</th>\n",
       "      <th>lon_lowestmode</th>\n",
       "      <th>sensitivity</th>\n",
       "      <th>quality_flag</th>\n",
       "      <th>rh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19640016800109452</td>\n",
       "      <td>-13.730080</td>\n",
       "      <td>-44.150036</td>\n",
       "      <td>0.946068</td>\n",
       "      <td>1</td>\n",
       "      <td>[-5.090000152587891, -4.599999904632568, -4.23...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19640017000109453</td>\n",
       "      <td>-13.729666</td>\n",
       "      <td>-44.149719</td>\n",
       "      <td>0.942039</td>\n",
       "      <td>1</td>\n",
       "      <td>[-5.800000190734863, -5.28000020980835, -4.900...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19640017200109454</td>\n",
       "      <td>-13.729252</td>\n",
       "      <td>-44.149403</td>\n",
       "      <td>0.937416</td>\n",
       "      <td>1</td>\n",
       "      <td>[-4.75, -4.300000190734863, -3.930000066757202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19640017400109455</td>\n",
       "      <td>-13.728838</td>\n",
       "      <td>-44.149086</td>\n",
       "      <td>0.941184</td>\n",
       "      <td>1</td>\n",
       "      <td>[-5.199999809265137, -4.710000038146973, -4.30...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19640017600109456</td>\n",
       "      <td>-13.728425</td>\n",
       "      <td>-44.148770</td>\n",
       "      <td>0.932089</td>\n",
       "      <td>1</td>\n",
       "      <td>[-5.050000190734863, -4.599999904632568, -4.26...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         shot_number  lat_lowestmode  lon_lowestmode  sensitivity  \\\n",
       "0  19640016800109452      -13.730080      -44.150036     0.946068   \n",
       "1  19640017000109453      -13.729666      -44.149719     0.942039   \n",
       "2  19640017200109454      -13.729252      -44.149403     0.937416   \n",
       "3  19640017400109455      -13.728838      -44.149086     0.941184   \n",
       "4  19640017600109456      -13.728425      -44.148770     0.932089   \n",
       "\n",
       "   quality_flag                                                 rh  \n",
       "0             1  [-5.090000152587891, -4.599999904632568, -4.23...  \n",
       "1             1  [-5.800000190734863, -5.28000020980835, -4.900...  \n",
       "2             1  [-4.75, -4.300000190734863, -3.930000066757202...  \n",
       "3             1  [-5.199999809265137, -4.710000038146973, -4.30...  \n",
       "4             1  [-5.050000190734863, -4.599999904632568, -4.26...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layers=['shot_number','lat_lowestmode','lon_lowestmode', 'sensitivity','quality_flag','rh']\n",
    "dfbox=generateBoxDataFrame([h5_2A],layers,idsbox)\n",
    "dfbox.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following example calculates the maximum layer rh and is added in the dataframe. Other variables (e.g median, mean, std, pcr75, pcr90) can also be calculated for layers of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "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>shot_number</th>\n",
       "      <th>lat_lowestmode</th>\n",
       "      <th>lon_lowestmode</th>\n",
       "      <th>sensitivity</th>\n",
       "      <th>quality_flag</th>\n",
       "      <th>rh</th>\n",
       "      <th>max_rh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19640016800109452</td>\n",
       "      <td>-13.730080</td>\n",
       "      <td>-44.150036</td>\n",
       "      <td>0.946068</td>\n",
       "      <td>1</td>\n",
       "      <td>[-5.090000152587891, -4.599999904632568, -4.23...</td>\n",
       "      <td>4.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19640017000109453</td>\n",
       "      <td>-13.729666</td>\n",
       "      <td>-44.149719</td>\n",
       "      <td>0.942039</td>\n",
       "      <td>1</td>\n",
       "      <td>[-5.800000190734863, -5.28000020980835, -4.900...</td>\n",
       "      <td>4.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19640017200109454</td>\n",
       "      <td>-13.729252</td>\n",
       "      <td>-44.149403</td>\n",
       "      <td>0.937416</td>\n",
       "      <td>1</td>\n",
       "      <td>[-4.75, -4.300000190734863, -3.930000066757202...</td>\n",
       "      <td>4.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19640017400109455</td>\n",
       "      <td>-13.728838</td>\n",
       "      <td>-44.149086</td>\n",
       "      <td>0.941184</td>\n",
       "      <td>1</td>\n",
       "      <td>[-5.199999809265137, -4.710000038146973, -4.30...</td>\n",
       "      <td>4.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19640017600109456</td>\n",
       "      <td>-13.728425</td>\n",
       "      <td>-44.148770</td>\n",
       "      <td>0.932089</td>\n",
       "      <td>1</td>\n",
       "      <td>[-5.050000190734863, -4.599999904632568, -4.26...</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         shot_number  lat_lowestmode  lon_lowestmode  sensitivity  \\\n",
       "0  19640016800109452      -13.730080      -44.150036     0.946068   \n",
       "1  19640017000109453      -13.729666      -44.149719     0.942039   \n",
       "2  19640017200109454      -13.729252      -44.149403     0.937416   \n",
       "3  19640017400109455      -13.728838      -44.149086     0.941184   \n",
       "4  19640017600109456      -13.728425      -44.148770     0.932089   \n",
       "\n",
       "   quality_flag                                                 rh  max_rh  \n",
       "0             1  [-5.090000152587891, -4.599999904632568, -4.23...    4.94  \n",
       "1             1  [-5.800000190734863, -5.28000020980835, -4.900...    4.15  \n",
       "2             1  [-4.75, -4.300000190734863, -3.930000066757202...    4.79  \n",
       "3             1  [-5.199999809265137, -4.710000038146973, -4.30...    4.60  \n",
       "4             1  [-5.050000190734863, -4.599999904632568, -4.26...    4.75  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfbox['max_rh'] = [np.max(i) for i in dfbox['rh']]\n",
    "dfbox.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the data frame is created, export files in the different formats (csv, shp, tif) based on GDAL. These may all be opened in other programs (e.g. Excel, QGIS).\n",
    "\n",
    "#### Dataframe to CSV:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully created\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'DataFrame already converted'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2csv(dfbox,filename='Box_GEDI02_A',outdir='data/formats/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CSV to SHP:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully created\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'file already converted'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv2shp('data/formats/Box_GEDI02_A.csv',filename='Box_GEDI02_A',outdir='data/formats/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SHP to TIF:\n",
    "\n",
    "Note: Given that GEDI is 25m the pixel used must be of 0.00025. Float is recommended. Define the variable of interest for band(1) (e.g. height in centimeters or meters)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully created\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'file already converted'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shp2tiff('data/formats/Box_GEDI02_A.shp', layername='max_rh', \n",
    "        pixelsize='0.00025', \n",
    "        nodata='225', \n",
    "        ot='float',\n",
    "        filename='Box_GEDI02_A',\n",
    "        outdir='data/formats/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization and Analysis\n",
    "\n",
    "Now that the file has been created as shp or tif, visualization and analysis are possible.\n",
    "\n",
    "### Please note you will need these packages:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import plotly.express as px\n",
    "import folium"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following data analyses are from  Democratic Republic of the Congo where various variables were calculated that pertain to GEDI product 2A. Variables tcc, tch75, tch90, tch95, tch100, are the percentile result from layer rh."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To visualize in shp format and an attributes table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "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>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>year</th>\n",
       "      <th>day</th>\n",
       "      <th>tcc</th>\n",
       "      <th>tch75</th>\n",
       "      <th>tch90</th>\n",
       "      <th>tch95</th>\n",
       "      <th>tch100</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-4.000107</td>\n",
       "      <td>20.827982</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>POINT (20.82798 -4.00011)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-4.001792</td>\n",
       "      <td>20.829191</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>POINT (20.82919 -4.00179)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4.003045</td>\n",
       "      <td>20.830092</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>POINT (20.83009 -4.00304)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-4.003466</td>\n",
       "      <td>20.830394</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>POINT (20.83039 -4.00347)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-4.003889</td>\n",
       "      <td>20.830697</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>POINT (20.83070 -4.00389)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lat        lon    year    day   tcc  tch75  tch90  tch95  tch100  \\\n",
       "0 -4.000107  20.827982  2019.0  140.0  32.0   11.0   14.0   15.0    18.0   \n",
       "1 -4.001792  20.829191  2019.0  140.0  63.0   13.0   23.0   26.0    30.0   \n",
       "2 -4.003045  20.830092  2019.0  140.0  86.0   11.0   13.0   14.0    16.0   \n",
       "3 -4.003466  20.830394  2019.0  140.0  81.0   12.0   18.0   19.0    22.0   \n",
       "4 -4.003889  20.830697  2019.0  140.0  35.0   17.0   28.0   29.0    32.0   \n",
       "\n",
       "                    geometry  \n",
       "0  POINT (20.82798 -4.00011)  \n",
       "1  POINT (20.82919 -4.00179)  \n",
       "2  POINT (20.83009 -4.00304)  \n",
       "3  POINT (20.83039 -4.00347)  \n",
       "4  POINT (20.83070 -4.00389)  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "GEDI_shp = gpd.read_file('data/formats/DRC.shp')\n",
    "plotSHP(GEDI_shp,'tch75',colormap='Spectral_r')\n",
    "GEDI_shp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3D Graph of Latitude, Longitude, and Height(tch75) of Congo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2af8b3b5cd90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1=plt.figure(figsize=(15,15))\n",
    "ax=fig1.add_subplot(211,projection='3d')\n",
    "data = [GEDI_shp['lat'], GEDI_shp['lon'], GEDI_shp['tch75']]\n",
    "AX = ax.scatter(*data, c=data[2], vmin=0, vmax=30, cmap='BuGn')\n",
    "ax.view_init(15,30)\n",
    "plt.colorbar(AX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To visualize a Height (tch75) Histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "255.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "GEDI_raster = 'data/formats/tch75_DRC.tif'\n",
    "histRaster(GEDI_raster,bins=40)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}