1. ICESat-2_Granules.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b355efa2-dfa8-4763-8fed-9e99db8ce750",
   "metadata": {},
   "source": [
    "# Arctic Lake Bathymetry\n",
    "#### Identify ICESat-13 ATL 13 Granules, convert HDF to CSV - ICESat\n",
    "#### Melanie Frost\n",
    "#### 4/18/2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "654f79d6-b161-4658-8534-aa2f5c969816",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import json\n",
    "import warnings\n",
    "\n",
    "import certifi\n",
    "import urllib3\n",
    "from urllib.parse import urlencode\n",
    "\n",
    "from pprint import pprint\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from dateutil import parser\n",
    "import h5py\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b8869db-258b-4765-9290-6ead0f475c49",
   "metadata": {},
   "source": [
    "# Get list of ATL13 granule names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b33ce1b2-e2f2-420b-a254-46c615ff1bc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# -----------------------------------------------------------------------------\n",
    "# class CmrProcess\n",
    "#\n",
    "# @author: Caleb Spradlin, caleb.s.spradlin@nasa.gov\n",
    "# @version: 12.30.2021\n",
    "#\n",
    "# https://cmr.earthdata.nasa.gov/search/\n",
    "# https://cmr.earthdata.nasa.gov/search/site/docs/search/api.html\n",
    "# -----------------------------------------------------------------------------\n",
    "class CmrProcess(object):\n",
    "\n",
    "    CMR_BASE_URL = 'https://cmr.earthdata.nasa.gov' +\\\n",
    "        '/search/granules.umm_json_v1_4?'\n",
    "\n",
    "    # Range for valid lon/lat\n",
    "    LATITUDE_RANGE = (-90, 90)\n",
    "    LONGITUDE_RANGE = (-180, 180)\n",
    "\n",
    "    # -------------------------------------------------------------------------\n",
    "    # __init__\n",
    "    # -------------------------------------------------------------------------\n",
    "    def __init__(self,\n",
    "                 mission,\n",
    "                 dateTime,\n",
    "                 lonLat=None,\n",
    "                 error=False,\n",
    "                 dayNightFlag='',\n",
    "                 pageSize=150,\n",
    "                 maxPages=50):\n",
    "\n",
    "        self._error = error\n",
    "        self._dateTime = dateTime\n",
    "        self._mission = mission\n",
    "        self._pageSize = pageSize\n",
    "        self._maxPages = maxPages\n",
    "        \n",
    "        self._lonLat = lonLat\n",
    "        self._dayNightFlag = dayNightFlag\n",
    "\n",
    "    # -------------------------------------------------------------------------\n",
    "    # run()\n",
    "    #\n",
    "    # Given a set of parameters on init (time, location, mission), search for\n",
    "    # the most relevant file. This uses CMR to search metadata for\n",
    "    # relevant matches.\n",
    "    # -------------------------------------------------------------------------\n",
    "    def run(self):\n",
    "        print('Starting query')\n",
    "        outout = set()\n",
    "        for i in range(self._maxPages):\n",
    "            \n",
    "            d, e = self._cmrQuery(pageNum=i+1)\n",
    "            \n",
    "            if e and i > 1:\n",
    "                return sorted(list(outout))\n",
    "            \n",
    "            if not e:\n",
    "                print('Results found on page: {}'.format(i+1))\n",
    "                out = [r['file_url'] for r in d.values()]\n",
    "                outout.update(out)\n",
    "                \n",
    "        outout = sorted(list(outout))\n",
    "        return outout\n",
    "        \n",
    "\n",
    "    # -------------------------------------------------------------------------\n",
    "    # cmrQuery()\n",
    "    #\n",
    "    # Search the Common Metadata Repository(CMR) for a file that\n",
    "    # is a temporal and spatial match.\n",
    "    # -------------------------------------------------------------------------\n",
    "    def _cmrQuery(self, pageNum=1):\n",
    "\n",
    "        requestDictionary = self._buildRequest(pageNum=pageNum)\n",
    "        totalHits, resultDictionary = self._sendRequest(requestDictionary)\n",
    "\n",
    "        if self._error:\n",
    "            return None, self._error\n",
    "\n",
    "        if totalHits <= 0:\n",
    "            print('No hits on page number: {}, ending search.'.format(pageNum))\n",
    "            #warnings.warn(msg)\n",
    "            return None, True\n",
    "\n",
    "        resultDictionaryProcessed = self._processRequest(resultDictionary)\n",
    "        return resultDictionaryProcessed, self._error\n",
    "\n",
    "    # -------------------------------------------------------------------------\n",
    "    # buildRequest()\n",
    "    #\n",
    "    # Build a dictionary based off of parameters given on init.\n",
    "    # This dictionary will be used to encode the http request to search\n",
    "    # CMR.\n",
    "    # -------------------------------------------------------------------------\n",
    "    def _buildRequest(self, pageNum=1):\n",
    "        requestDict = dict()\n",
    "        requestDict['page_num'] = pageNum\n",
    "        requestDict['page_size'] = self._pageSize\n",
    "        requestDict['concept_id'] = self._mission\n",
    "        requestDict['bounding_box'] = self._lonLat\n",
    "        requestDict['day_night_flag'] = self._dayNightFlag\n",
    "        requestDict['temporal'] = self._dateTime\n",
    "        return requestDict\n",
    "\n",
    "    # -------------------------------------------------------------------------\n",
    "    # _sendRequest\n",
    "    #\n",
    "    # Send an http request to the CMR server.\n",
    "    # Decode data and count number of hits from request.\n",
    "    # -------------------------------------------------------------------------\n",
    "    def _sendRequest(self, requestDictionary):\n",
    "        with urllib3.PoolManager(cert_reqs='CERT_REQUIRED',\n",
    "                                 ca_certs=certifi.where()) as httpPoolManager:\n",
    "            encodedParameters = urlencode(requestDictionary, doseq=True)\n",
    "            requestUrl = self.CMR_BASE_URL + encodedParameters\n",
    "            try:\n",
    "                requestResultPackage = httpPoolManager.request('GET',\n",
    "                                                               requestUrl)\n",
    "            except urllib3.exceptions.MaxRetryError:\n",
    "                self._error = True\n",
    "                return 0, None\n",
    "\n",
    "            requestResultData = json.loads(\n",
    "                requestResultPackage.data.decode('utf-8'))\n",
    "            status = int(requestResultPackage.status)\n",
    "\n",
    "            if not status == 400:\n",
    "                totalHits = len(requestResultData['items'])\n",
    "                return totalHits, requestResultData\n",
    "\n",
    "            else:\n",
    "                msg = 'CMR Query: Client or server error: ' + \\\n",
    "                    'Status: {}, Request URL: {}, Params: {}'.format(\n",
    "                        str(status), requestUrl, encodedParameters)\n",
    "                warnings.warn(msg)\n",
    "                return 0, None\n",
    "\n",
    "    # -------------------------------------------------------------------------\n",
    "    # _processRequest\n",
    "    #\n",
    "    # For each result in the CMR query, unpackage relevant information to\n",
    "    # a dictionary. While doing so set flags if data is not desirable (too\n",
    "    # close to edge of dataset).\n",
    "    #\n",
    "    #  REVIEW: Make the hard-coded names class constants? There are a lot...\n",
    "    # -------------------------------------------------------------------------\n",
    "    def _processRequest(self, resultDict):\n",
    "\n",
    "        resultDictProcessed = dict()\n",
    "\n",
    "        for hit in resultDict['items']:\n",
    "\n",
    "            fileName = hit['umm']['RelatedUrls'][0]['URL'].split(\n",
    "                '/')[-1]\n",
    "\n",
    "            # ---\n",
    "            # These are hardcoded here because the only time these names will\n",
    "            # ever change is if we changed which format of metadata we wanted\n",
    "            # the CMR results back in.\n",
    "            #\n",
    "            # These could be placed as class constants in the future.\n",
    "            # ---\n",
    "            fileUrl = hit['umm']['RelatedUrls'][0]['URL']\n",
    "            temporalRange = hit['umm']['TemporalExtent']['RangeDateTime']\n",
    "            dayNight = hit['umm']['DataGranule']['DayNightFlag']\n",
    "\n",
    " \n",
    "            spatialExtent = hit['umm']['SpatialExten' +\n",
    "                                          't']['HorizontalSpatialDom' +\n",
    "                                               'ain']\n",
    "\n",
    "            key = fileName\n",
    "\n",
    "            resultDictProcessed[key] = {\n",
    "                'file_name': fileName,\n",
    "                'file_url': fileUrl,\n",
    "                'temporal_range': temporalRange,\n",
    "                'spatial_extent': spatialExtent,\n",
    "                'day_night_flag': dayNight}\n",
    "\n",
    "        return resultDictProcessed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a14fffe-5e25-414e-8035-7cac79e7e1f6",
   "metadata": {},
   "source": [
    "Alaska North Slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "82deb8aa-3a59-417d-8aff-f154e912486c",
   "metadata": {},
   "outputs": [],
   "source": [
    "search_dict_AKNS = {\n",
    "    'site_name': 'Alaska North Slope',\n",
    "    'bbox': [-168, 67.3, -141, 73], \n",
    "    'minmonth': \"07\",\n",
    "    'maxmonth': \"09\",\n",
    "    'years_list': [2018,2019,2020,2021,2022]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "37a66979-9f03-4592-ae42-2a0ce25b94d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose a site\n",
    "search_dict = search_dict_AKNS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "55cfef1c-170d-4da2-a56d-e9634810dc83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find this at https://search.earthdata.nasa.gov/\n",
    "# search for ATL## in search box, click on granule, click on \"view details\" \n",
    "COLLECTID_ATL13_V5 = \"C2153575088-NSIDC_CPRD\"\n",
    "\n",
    "# Page size: 150, number of results returned by page.\n",
    "PAGESIZE = 150 \n",
    "# Max page, number of pages to return before ending query.\n",
    "MAXPAGE = 60\n",
    "# Total max results will be PAGESIZE * MAXPAG\n",
    "\n",
    "cmrP_list = []\n",
    "for YEAR in search_dict['years_list']:\n",
    "    cmrP = CmrProcess(mission = COLLECTID_ATL13_V5, \n",
    "                      dateTime=f\"{YEAR}-{search_dict['minmonth']}-01T00:00:00Z,{YEAR}-{search_dict['maxmonth']}-30T23:59:59Z\", \n",
    "                      lonLat = ','.join(str(e) for e in search_dict['bbox']),\n",
    "                      pageSize=PAGESIZE,\n",
    "                      maxPages=MAXPAGE)\n",
    "    cmrP_list.append(cmrP)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aea3b596-4aa8-4884-9c67-4668875ddff2",
   "metadata": {},
   "source": [
    "Run Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "21fb98c9-d4b7-4504-9977-6f6c90dfd8aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting query\n",
      "No hits on page number: 1, ending search.\n",
      "No hits on page number: 2, ending search.\n",
      "No hits on page number: 3, ending search.\n",
      "Starting query\n",
      "Results found on page: 1\n",
      "Results found on page: 2\n",
      "No hits on page number: 3, ending search.\n",
      "Starting query\n",
      "Results found on page: 1\n",
      "Results found on page: 2\n",
      "No hits on page number: 3, ending search.\n",
      "Starting query\n",
      "Results found on page: 1\n",
      "Results found on page: 2\n",
      "No hits on page number: 3, ending search.\n",
      "Starting query\n",
      "Results found on page: 1\n",
      "Results found on page: 2\n",
      "No hits on page number: 3, ending search.\n"
     ]
    }
   ],
   "source": [
    "resultList = [cmrP.run() for cmrP in cmrP_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "63261760-2848-4299-a5c6-cb2b3adab800",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "782 granules in search results list\n"
     ]
    }
   ],
   "source": [
    "len(resultList[0])\n",
    "atl13_granule_list = [item for sublist in resultList for item in sublist]\n",
    "print(f\"{len(atl13_granule_list)} granules in search results list\")\n",
    "\n",
    "# Get list of just granule names without paths\n",
    "atl13_granule_list_nopath = [g.split('/')[-1] for g in atl13_granule_list]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "100fd5e4-473b-49e0-8a89-52212acdf0ba",
   "metadata": {},
   "source": [
    "Here is a dataframe of the granule paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fdbb0b0c-3c41-4798-886c-5f15df7d1b70",
   "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>path</th>\n",
       "      <th>site_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://data.nsidc.earthdatacloud.nasa.gov/nsi...</td>\n",
       "      <td>Alaska North Slope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://data.nsidc.earthdatacloud.nasa.gov/nsi...</td>\n",
       "      <td>Alaska North Slope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://data.nsidc.earthdatacloud.nasa.gov/nsi...</td>\n",
       "      <td>Alaska North Slope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://data.nsidc.earthdatacloud.nasa.gov/nsi...</td>\n",
       "      <td>Alaska North Slope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://data.nsidc.earthdatacloud.nasa.gov/nsi...</td>\n",
       "      <td>Alaska North Slope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>https://data.nsidc.earthdatacloud.nasa.gov/nsi...</td>\n",
       "      <td>Alaska North Slope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>https://data.nsidc.earthdatacloud.nasa.gov/nsi...</td>\n",
       "      <td>Alaska North Slope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>https://data.nsidc.earthdatacloud.nasa.gov/nsi...</td>\n",
       "      <td>Alaska North Slope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>https://data.nsidc.earthdatacloud.nasa.gov/nsi...</td>\n",
       "      <td>Alaska North Slope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>https://data.nsidc.earthdatacloud.nasa.gov/nsi...</td>\n",
       "      <td>Alaska North Slope</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>782 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  path           site_name\n",
       "0    https://data.nsidc.earthdatacloud.nasa.gov/nsi...  Alaska North Slope\n",
       "1    https://data.nsidc.earthdatacloud.nasa.gov/nsi...  Alaska North Slope\n",
       "2    https://data.nsidc.earthdatacloud.nasa.gov/nsi...  Alaska North Slope\n",
       "3    https://data.nsidc.earthdatacloud.nasa.gov/nsi...  Alaska North Slope\n",
       "4    https://data.nsidc.earthdatacloud.nasa.gov/nsi...  Alaska North Slope\n",
       "..                                                 ...                 ...\n",
       "777  https://data.nsidc.earthdatacloud.nasa.gov/nsi...  Alaska North Slope\n",
       "778  https://data.nsidc.earthdatacloud.nasa.gov/nsi...  Alaska North Slope\n",
       "779  https://data.nsidc.earthdatacloud.nasa.gov/nsi...  Alaska North Slope\n",
       "780  https://data.nsidc.earthdatacloud.nasa.gov/nsi...  Alaska North Slope\n",
       "781  https://data.nsidc.earthdatacloud.nasa.gov/nsi...  Alaska North Slope\n",
       "\n",
       "[782 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atl13_h5_fn_df = pd.DataFrame(atl13_granule_list, columns = ['path'])\n",
    "atl13_h5_fn_df['site_name'] = search_dict['site_name']\n",
    "\n",
    "atl13_h5_fn_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1f003004-448e-419e-91c8-eff68ad1e72d",
   "metadata": {},
   "outputs": [],
   "source": [
    "granule_test = atl13_granule_list_nopath[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0511968c-0ec4-4292-92a9-627ef53fd3ed",
   "metadata": {},
   "source": [
    "# Extract HDF files as CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "44202732-1129-4cee-bbdb-8728ba45d100",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/panfs/ccds02/nobackup/people/mfrost2/projects/AKNS\n",
      "/nfs4m/css/curated01/icesat-2/data/ATLAS/ATL13.005\n"
     ]
    }
   ],
   "source": [
    "print(os.getcwd())\n",
    "directory = r'/css/icesat-2/ATLAS/ATL13.005'\n",
    "files = os.listdir(directory)\n",
    "os.chdir(directory)\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "db2306f2-9932-4294-8f72-2f92d6c39baa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [cycle, id, size, type, cloud_flag, bkgrd_flag, shallow_flag, wind_flag, rgt, seg_lat, seg_lon, snow_ice_flag, wave_flag, depth, anomalies, beam, start_date, qual0, qual1, qual2, qual3, orientation, beg_lat, beg_lon, end_lat, end_lon]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "#make empty dataframe\n",
    "df = pd.DataFrame(columns = ['cycle', 'id', 'size', 'type', 'cloud_flag', 'bkgrd_flag', \n",
    "                             'shallow_flag', 'wind_flag', 'rgt', 'seg_lat', 'seg_lon',\n",
    "                             'snow_ice_flag', 'wave_flag', 'depth', 'anomalies', 'beam',\n",
    "                             'start_date', 'qual0', 'qual1', 'qual2', 'qual3', 'sc_orient',\n",
    "                             'beg_lat', 'beg_lon', 'end_lat', 'end_lon'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c83e36aa-dcc6-4c77-8a7f-2ec413b61e0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "year = slice(6,10)\n",
    "month = slice(10,12)\n",
    "day = slice(12,14)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9a83bfb7-ff26-4c21-8f51-dfe08f8c0563",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Doing above, but as a function\n",
    "# def subfolder(file):\n",
    "#     year = slice(6,10)\n",
    "#     month = slice(10,12)\n",
    "#     day = slice(12,14)\n",
    "#     subdir = str(hdfile[year]+\".\"+hdfile[month]+\".\"+hdfile[day]+\"/\"+hdfile)\n",
    "#     print(subdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f0ca10e6-d73b-4806-9546-161aefa95b09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019.07.09/ATL13_20190709155410_01770401_005_01.h5\n",
      "2019.07.09/ATL13_20190709221119_01810401_005_01.h5\n",
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      "2022.08.27/ATL13_20220827053856_10131601_005_01.h5\n",
      "2022.08.27/ATL13_20220827181315_10211601_005_01.h5\n",
      "2022.08.28/ATL13_20220828003024_10251601_005_01.h5\n",
      "2022.08.28/ATL13_20220828130443_10331601_005_01.h5\n",
      "2022.08.29/ATL13_20220829013902_10411601_005_01.h5\n",
      "2022.08.29/ATL13_20220829141320_10491601_005_01.h5\n",
      "2022.08.30/ATL13_20220830024739_10571601_005_01.h5\n",
      "2022.08.30/ATL13_20220830152158_10651601_005_02.h5\n",
      "2022.08.31/ATL13_20220831035616_10731601_005_01.h5\n",
      "2022.08.31/ATL13_20220831163035_10811601_005_01.h5\n",
      "2022.09.01/ATL13_20220901050453_10891601_005_01.h5\n",
      "2022.09.01/ATL13_20220901173912_10971601_005_01.h5\n",
      "2022.09.01/ATL13_20220901235621_11011601_005_01.h5\n",
      "2022.09.02/ATL13_20220902061330_11051601_005_01.h5\n",
      "2022.09.02/ATL13_20220902123040_11091601_005_01.h5\n",
      "2022.09.02/ATL13_20220902184750_11131601_005_01.h5\n",
      "2022.09.03/ATL13_20220903010500_11171601_005_01.h5\n",
      "2022.09.03/ATL13_20220903133919_11251601_005_01.h5\n",
      "2022.09.04/ATL13_20220904021339_11331601_005_01.h5\n",
      "2022.09.04/ATL13_20220904144758_11411601_005_01.h5\n",
      "2022.09.05/ATL13_20220905032217_11491601_005_01.h5\n",
      "2022.09.05/ATL13_20220905155636_11571601_005_01.h5\n",
      "2022.09.06/ATL13_20220906043055_11651601_005_01.h5\n",
      "2022.09.06/ATL13_20220906170514_11731601_005_01.h5\n",
      "2022.09.06/ATL13_20220906232223_11771601_005_01.h5\n",
      "2022.09.07/ATL13_20220907053933_11811601_005_01.h5\n",
      "2022.09.07/ATL13_20220907115642_11851601_005_01.h5\n",
      "2022.09.07/ATL13_20220907181351_11891601_005_01.h5\n",
      "2022.09.08/ATL13_20220908003101_11931601_005_01.h5\n",
      "2022.09.08/ATL13_20220908130519_12011601_005_01.h5\n",
      "2022.09.09/ATL13_20220909013938_12091601_005_01.h5\n",
      "2022.09.09/ATL13_20220909141357_12171601_005_01.h5\n",
      "2022.09.10/ATL13_20220910024816_12251601_005_01.h5\n",
      "2022.09.10/ATL13_20220910152236_12331601_005_01.h5\n",
      "2022.09.11/ATL13_20220911035655_12411601_005_01.h5\n",
      "2022.09.11/ATL13_20220911163114_12491601_005_01.h5\n",
      "2022.09.12/ATL13_20220912050533_12571601_005_01.h5\n",
      "2022.09.12/ATL13_20220912173952_12651601_005_01.h5\n",
      "2022.09.12/ATL13_20220912235701_12691601_005_01.h5\n",
      "2022.09.13/ATL13_20220913123120_12771601_005_01.h5\n",
      "2022.09.14/ATL13_20220914010539_12851601_005_01.h5\n",
      "2022.09.14/ATL13_20220914133957_12931601_005_01.h5\n",
      "2022.09.15/ATL13_20220915021416_13011601_005_01.h5\n",
      "2022.09.15/ATL13_20220915144835_13091601_005_01.h5\n",
      "2022.09.16/ATL13_20220916032253_13171601_005_01.h5\n",
      "2022.09.16/ATL13_20220916155712_13251601_005_01.h5\n",
      "2022.09.17/ATL13_20220917043132_13331601_005_01.h5\n",
      "2022.09.17/ATL13_20220917170551_13411601_005_01.h5\n",
      "2022.09.17/ATL13_20220917232301_13451601_005_01.h5\n",
      "2022.09.18/ATL13_20220918054011_13491601_005_01.h5\n",
      "2022.09.18/ATL13_20220918115721_13531601_005_01.h5\n",
      "2022.09.18/ATL13_20220918181430_13571601_005_01.h5\n",
      "2022.09.19/ATL13_20220919003140_13611601_005_01.h5\n",
      "2022.09.19/ATL13_20220919130559_13691601_005_01.h5\n",
      "2022.09.20/ATL13_20220920014019_13771601_005_01.h5\n",
      "2022.09.20/ATL13_20220920141438_13851601_005_01.h5\n",
      "2022.09.21/ATL13_20220921011440_00051701_005_01.h5\n",
      "2022.09.21/ATL13_20220921134859_00131701_005_01.h5\n",
      "2022.09.22/ATL13_20220922022318_00211701_005_01.h5\n",
      "2022.09.22/ATL13_20220922145736_00291701_005_01.h5\n",
      "2022.09.23/ATL13_20220923033157_00371701_005_01.h5\n",
      "2022.09.23/ATL13_20220923160615_00451701_005_01.h5\n",
      "2022.09.24/ATL13_20220924044034_00531701_005_01.h5\n",
      "2022.09.24/ATL13_20220924171453_00611701_005_01.h5\n",
      "2022.09.24/ATL13_20220924233202_00651701_005_01.h5\n",
      "2022.09.25/ATL13_20220925120620_00731701_005_01.h5\n",
      "2022.09.26/ATL13_20220926004039_00811701_005_01.h5\n",
      "2022.09.26/ATL13_20220926131457_00891701_005_01.h5\n",
      "2022.09.27/ATL13_20220927014917_00971701_005_01.h5\n",
      "2022.09.27/ATL13_20220927142336_01051701_005_01.h5\n",
      "2022.09.28/ATL13_20220928025756_01131701_005_01.h5\n",
      "2022.09.28/ATL13_20220928153215_01211701_005_01.h5\n",
      "2022.09.29/ATL13_20220929040634_01291701_005_01.h5\n",
      "2022.09.29/ATL13_20220929164053_01371701_005_01.h5\n",
      "2022.09.29/ATL13_20220929225802_01411701_005_01.h5\n",
      "2022.09.30/ATL13_20220930113221_01491701_005_01.h5\n",
      "(1282216, 27)\n",
      "<bound method NDFrame.head of        cycle       id size type cloud_flag  bkgrd_flag shallow_flag wind_flag  \\\n",
      "24539      4  4009051    0    7          0           2           -1         0   \n",
      "24540      4  4009051    0    7          0           2           -1         0   \n",
      "24541      4  4009051    0    7          0           2           -1         0   \n",
      "24542      4  4009051    0    7          0           2           -1         0   \n",
      "24543      4  4009051    0    7          0           2           -1         0   \n",
      "...      ...      ...  ...  ...        ...         ...          ...       ...   \n",
      "127927    17   253947    6    1          1  2147483647            4         4   \n",
      "127928    17   253947    6    1          1  2147483647            4         4   \n",
      "127929    17   253947    6    1          1  2147483647            4         4   \n",
      "127930    17   253947    6    1          1  2147483647            4         4   \n",
      "127931    17   253947    6    1          1  2147483647            4         4   \n",
      "\n",
      "        rgt    seg_lat  ...  qual0 qual1  qual2  qual3 orientation    beg_lat  \\\n",
      "24539   183  70.712450  ...    109     0      0      0         NaN  70.712665   \n",
      "24540   183  70.711955  ...    117     0      0      0         NaN  70.712209   \n",
      "24541   183  70.711497  ...    106     0      0      0         NaN  70.711663   \n",
      "24542   183  70.711161  ...    110     0      0      0         NaN  70.711338   \n",
      "24543   183  70.710705  ...    117     0      0      0         NaN  70.710951   \n",
      "...     ...        ...  ...    ...   ...    ...    ...         ...        ...   \n",
      "127927  152  69.646846  ...    136     0      0      0         NaN  69.647129   \n",
      "127928  152  69.646107  ...    154     0      0      0         NaN  69.646517   \n",
      "127929  152  69.645491  ...    142     0      0      0         NaN  69.645777   \n",
      "127930  152  69.644953  ...    124     0      0      0         NaN  69.645230   \n",
      "127931  152  69.644454  ...    127     0      0      0         NaN  69.644706   \n",
      "\n",
      "           beg_lon    end_lat     end_lon sc_orient  \n",
      "24539  -160.126315  70.712665 -160.126492       0.0  \n",
      "24540  -160.126492  70.712209 -160.126707       0.0  \n",
      "24541  -160.126707  70.711663 -160.126835       0.0  \n",
      "24542  -160.126835  70.711338 -160.126988       0.0  \n",
      "24543  -160.126988  70.710951 -160.127163       0.0  \n",
      "...            ...        ...         ...       ...  \n",
      "127927 -148.840391  69.647129 -148.840586       1.0  \n",
      "127928 -148.840582  69.646517 -148.840838       1.0  \n",
      "127929 -148.840838  69.645777 -148.841017       1.0  \n",
      "127930 -148.841086  69.645230 -148.841189       1.0  \n",
      "127931 -148.841191  69.644706 -148.841362       1.0  \n",
      "\n",
      "[1282216 rows x 27 columns]>\n"
     ]
    }
   ],
   "source": [
    "for hdfile in atl13_granule_list_nopath:\n",
    "    \n",
    "    subdir = str(hdfile[year]+\".\"+hdfile[month]+\".\"+hdfile[day]+\"/\"+hdfile)\n",
    "        \n",
    "    FILE_NAME = subdir\n",
    "    print(FILE_NAME)\n",
    "    #'C:\\ICESat\\granules3\\processed_ATL13_20190709221119_01810401_005_01.h5'\n",
    "    \n",
    "    #Get the beams of the granule\n",
    "    hf = h5py.File(FILE_NAME, 'r')\n",
    "    keys = (list(hf.keys()))\n",
    "    beams = [x for x in keys if x.startswith('gt')]\n",
    "    \n",
    "    #check beams\n",
    "    # print(beams)\n",
    "    \n",
    "    #cycle through desired variables\n",
    "    for beam in beams:\n",
    "        cycle = str(\"/\"+ beam + \"/cycle\")                       #Tracks the number of 91-day cycles in the mission, beginning with 01.\n",
    "        water_id = str(\"/\"+ beam + \"/inland_water_body_id\")     #HydroLAKES water body ID number\n",
    "        water_size = str(\"/\"+ beam + \"/inland_water_body_size\") #A=area, where 0=Not_Assigned, 1=A>10,000 sq km, 2=10,000>A>=1,000, 3=1,000>A>=100, 4=100>A>=10, 5=10>A>=1,6=1>A>=0.1, 7=0.01>A,\n",
    "        water_type = str(\"/\"+ beam + \"/inland_water_body_type\") #Type of Inland Water Body, where 1=Lake, 2=Known, Reservoir, 3=(Reserved for future use), 4=Ephemeral, Water, 5=River, 6=Estuary or Bay, 7=Coastal Water,\n",
    "        cloud_flag = str(\"/\"+ beam + \"/layer_flag_atl09\")       #Consolidated cloud flag: 0=likely clear, 1=likely cloudy\n",
    "        bkgrd_flag = str(\"/\"+ beam + \"/qf_bckgrd\")              #Describes the degree of background photons present in each short segment. 0:<=0.001, 1:<=0.01,2:<=0.05,3:<=0.1,4:<=0.3,5:<=0.5,6:>0.5\n",
    "        shallow_flag = str(\"/\"+ beam + \"/qf_bias_em\")           #The Electromagnetic Bias flag is set based on threshold checks for the estimated electromagnetic height bias.\n",
    "        wind_flag = str(\"/\"+ beam + \"/qf_bias_fit\")             #The height bias fit flag is set based on the value of the goodness of fit bias estimated as the difference between the centroid elevations of the \n",
    "                                                                # observed surface waterhistogram and fitted integrated water surface model histogram.\n",
    "        anomalies = str(\"/\"+ beam + \"/qf_subsurf_anomaly\")      #1 = Subsurface anomaly due to bottom likely; 2 = Subsurface signal may indicate bottom or\n",
    "                                                                # other anomaly; 3 = Possible subsurface anomaly\n",
    "        rgt = str(\"/\"+ beam + \"/rgt\")                           #the track on the earth at which a specified unit vector within the observatory is pointed. \n",
    "        seg_lat = str(\"/\"+ beam + \"/segment_lat\")               #Latitude of reporting location for all short segment statistics\n",
    "        seg_lon = str(\"/\"+ beam + \"/segment_lon\")               #Longitude of reporting location for all short segment statistics\n",
    "        snow_ice_flag = str(\"/\"+ beam + \"/snow_ice_atl09\")      #NOAA snow/ice flag scaled by ATL09 (0=ice-free water, 1=snow-free land, 2=snow, 3=ice\n",
    "        wave_flag = str(\"/\"+ beam + \"/stdev_water_surf\")        #Standard deviation of water surface, calculated over long segments \n",
    "        depth = str(\"/\"+ beam + \"/water_depth\")                 #Depth from the mean water surface to detected bottom.\n",
    "        qual = str(\"/\"+ beam + \"/segment_quality\")              # num of photons 1: nominal; 2: possible afterpulse; 3: possible impulse response; 4: possible TEP\n",
    "        beg_lat = str(\"/\"+ beam + \"/sseg_start_lat\")            #Latitude at which the short segment begins.\n",
    "        beg_lon = str(\"/\"+ beam + \"/sseg_start_lon\")            #Longitude at which the short segment begins.\n",
    "        end_lat = str(\"/\"+ beam + \"/sseg_end_lat\")              #Latitude at which the short segment ends.\n",
    "        end_lon = str(\"/\"+ beam + \"/sseg_end_lon\")              #Longitude at which the short segment ends.\n",
    "    \n",
    "        #print(beam)\n",
    "        \n",
    "        with h5py.File(FILE_NAME, mode='r') as f:\n",
    "\n",
    "             cyclevar = f[cycle]\n",
    "             cycles = cyclevar[:]\n",
    "             \n",
    "             idvar = f[water_id]\n",
    "             water_ids = idvar[:]\n",
    "            \n",
    "             sizevar = f[water_size]\n",
    "             water_sizes = sizevar[:]\n",
    "            \n",
    "             typevar = f[water_type]\n",
    "             water_types = typevar[:]            \n",
    "         \n",
    "             cloudvar = f[cloud_flag]\n",
    "             clouds = cloudvar[:]            \n",
    "          \n",
    "             bkgrdvar = f[bkgrd_flag]\n",
    "             bkgrds = bkgrdvar[:]            \n",
    "         \n",
    "             shallowvar = f[shallow_flag]\n",
    "             shallows = shallowvar[:]            \n",
    "         \n",
    "             windvar = f[wind_flag]\n",
    "             winds = windvar[:]            \n",
    "         \n",
    "             anomvar = f[anomalies]\n",
    "             anoms = anomvar[:]            \n",
    "         \n",
    "             rgtvar = f[rgt]\n",
    "             rgts = rgtvar[:]            \n",
    "         \n",
    "             slatvar = f[seg_lat]\n",
    "             slats = slatvar[:]            \n",
    "         \n",
    "             slonvar = f[seg_lon]\n",
    "             slons = slonvar[:]            \n",
    "         \n",
    "             sn_ic_var = f[snow_ice_flag]\n",
    "             sn_ics = sn_ic_var[:]   \n",
    "        \n",
    "             wavevar = f[wave_flag]\n",
    "             waves = wavevar[:]   \n",
    "         \n",
    "             depthvar = f[depth]\n",
    "             depths = depthvar[:]\n",
    "             units = depthvar.attrs['units']\n",
    "             long_name = depthvar.attrs['long_name']\n",
    "                \n",
    "             blatvar = f[beg_lat]\n",
    "             blats = blatvar[:]            \n",
    "         \n",
    "             blonvar = f[beg_lon]\n",
    "             blons = blonvar[:] \n",
    "                \n",
    "             elatvar = f[end_lat]\n",
    "             elats = blatvar[:]            \n",
    "         \n",
    "             elonvar = f[end_lon]\n",
    "             elons = elonvar[:] \n",
    "            \n",
    "             date_time_var = f[str(\"/ancillary_data/granule_start_utc\")]\n",
    "             date_time = date_time_var[:]\n",
    "            \n",
    "             orient_var = f[str(\"/orbit_info/sc_orient\")]\n",
    "             orients = orient_var[:]\n",
    "               \n",
    "            \n",
    "             qualvar = f[qual]\n",
    "             qual0 = qualvar[:,0]\n",
    "             qual1 = qualvar[:,1]\n",
    "             qual2 = qualvar[:,2]\n",
    "             qual3 = qualvar[:,3]\n",
    "        \n",
    "        #check lengths\n",
    "        #print(len(water_ids), len(orients), len(date_time), len(qual0))\n",
    "\n",
    "        df2 = pd.DataFrame({'cycle' : cycles, 'id' : water_ids, 'size' : water_sizes, \n",
    "                         'type' : water_types, 'cloud_flag' : clouds, 'bkgrd_flag' : bkgrds, \n",
    "                         'shallow_flag' : shallows, 'wind_flag' : winds, 'anomalies': anoms, \n",
    "                         'rgt' : rgts, 'seg_lat' : slats, 'seg_lon': slons, 'snow_ice_flag' : sn_ics, \n",
    "                         'wave_flag' : waves, 'depth' : depths, 'qual0' : qual0, 'qual1' : qual1, \n",
    "                         'qual2' : qual2, 'qual3' : qual3, \n",
    "                         'beg_lat' : blats, 'beg_lon': blons, 'end_lat': elats, 'end_lon' : elons})\n",
    "\n",
    "        #include beam in dataset\n",
    "        df2['beam'] = beam\n",
    "        \n",
    "        #Include datatetime in dataset\n",
    "        df2['start_date'] = parser.parse(date_time[0], fuzzy=True)\n",
    "        \n",
    "        #Include orientation in dataset\n",
    "        df2['sc_orient'] = orients[0]\n",
    "        \n",
    "        #bbox': [-168, 67.3, -141, 73]\n",
    "        #filter for bounding box\n",
    "        df2 = df2.loc[(df2['seg_lat'] >= 67.3) & (df2['seg_lat'] <= 73) & \n",
    "                      (df2['seg_lon'] <= -141) & (df2['seg_lon'] >= -168)]\n",
    "\n",
    "        df = pd.concat([df,df2])\n",
    "print(df.shape)\n",
    "print(df.head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "edb5362a-4b36-486e-b18a-ec9f5e92c1d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24539   2019-07-09 22:11:19+00:00\n",
       "24540   2019-07-09 22:11:19+00:00\n",
       "24541   2019-07-09 22:11:19+00:00\n",
       "24542   2019-07-09 22:11:19+00:00\n",
       "24543   2019-07-09 22:11:19+00:00\n",
       "Name: start_date, dtype: datetime64[ns, tzutc()]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['start_date'] = pd.to_datetime(df['start_date'])\n",
    "df['start_date'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fc14eb5a-1c6a-47d0-b3f2-b8387f45dae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1282216, 27)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3ea18558-edb1-4aa3-8e01-f407706ac8b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(r'/explore/nobackup/people/mfrost2/projects/AKNS/data/ICESat_AKNS.csv', sep=',', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "30864417-657d-487c-aed2-94c1299ee07b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "127927   2022-09-30 11:32:21+00:00\n",
       "127928   2022-09-30 11:32:21+00:00\n",
       "127929   2022-09-30 11:32:21+00:00\n",
       "127930   2022-09-30 11:32:21+00:00\n",
       "127931   2022-09-30 11:32:21+00:00\n",
       "Name: start_date, dtype: datetime64[ns, tzutc()]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24860e4a-638d-4b49-8401-ae94ba3cff29",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:earthml]",
   "language": "python",
   "name": "conda-env-earthml-py"
  },
  "language_info": {
   "codemirror_mode": {
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