strong-beams.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "879766a9-70fc-455d-8d80-3b4d46298022",
   "metadata": {},
   "source": [
    "ICESat-2 orientation can be: backward, forward, and transition. During transition, ICESat-2 is maneuvering between the two orientations. Science quality is potentially degraded while in transition mode. (Source: https://nsidc.org/sites/nsidc.org/files/technical-references/ICESat2_ATL03_data_dict_v005.pdf)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bffa1df3-38f0-4296-8f54-67e8bf660efd",
   "metadata": {},
   "source": [
    "The parameter \"/orbit_info/sc_orient\" in ATL03 denotes the orientation of ICESat-2.\n",
    "\n",
    "0 = backward\n",
    "\n",
    "1 = forward\n",
    "\n",
    "2 = transition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "769024d5-5a3d-4f5c-bd85-538b8e570612",
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4fbfd3d-3a29-480e-8fdb-92cf38f103c5",
   "metadata": {},
   "source": [
    "User input: \"strong\" or \"weak\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "28be40d5-692f-4576-967e-e449562e7528",
   "metadata": {},
   "outputs": [],
   "source": [
    "def which_beam(f, sc_orient, beam='both'):\n",
    "    if beam=='both':\n",
    "        # extracted_beam = \n",
    "        print(\"Subset both beams\")\n",
    "    elif beam=='strong':\n",
    "        extracted_beam = get_strong_beams(f, sc_orient)\n",
    "    elif beam=='weak':\n",
    "        extracted_beam = get_weak_beams(f, sc_orient)\n",
    "    else:\n",
    "        print('Input should be one of the following: \"both\", \"strong\" or \"weak\"')\n",
    "    return extracted_beam"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96b00683-310d-4732-94c7-5339e8a260ff",
   "metadata": {},
   "source": [
    "Script reads the variable \"/orbit_info/sc_orient\" to determine orientation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5cdbf181-6e38-4992-ae1e-0efa7f548ce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If input = \"strong\":\n",
    "#     If sc_orient = 0:\n",
    "#         subset gt1l, gt2l, gt3l\n",
    "#     elif sc_orient = 1:\n",
    "#         subset gt1r, gt2r, gt3r\n",
    "#     else:\n",
    "#         print(\"Error: transition phase\"), or other output?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "750a9ff0-220c-4e7d-a059-e6872313fffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_strong_beams(f, sc_orient):\n",
    "    if sc_orient == 0:\n",
    "        # subset gt1l, gt2l, gt3l\n",
    "        track = 'gt3l' # or 'gt1l', 'gt2l'\n",
    "        strong = pd.DataFrame(data={'lat': f[track + '/sea_ice_segments/latitude'][:],\n",
    "                                  'lon': f[track + '/sea_ice_segments/longitude'][:],\n",
    "                                  'elev': f[track + '/sea_ice_segments/heights/height_segment_height'][:]\n",
    "                                 }\n",
    "                           )\n",
    "    elif sc_orient == 1:\n",
    "        # subset gt1r, gt2r, gt3r\n",
    "        track = 'gt3r' # or 'gt1r', 'gt2r'\n",
    "        strong = pd.DataFrame(data={'lat': f[track + '/sea_ice_segments/latitude'][:],\n",
    "                                  'lon': f[track + '/sea_ice_segments/longitude'][:],\n",
    "                                  'elev': f[track + '/sea_ice_segments/heights/height_segment_height'][:]\n",
    "                                 }\n",
    "                           )\n",
    "    else:\n",
    "        print(\"Error: transition phase\") # or other output?\n",
    "        strong = pd.DataFrame()\n",
    "    return strong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c95a2995-c5a2-496f-93b3-a44ad188d75e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If input = \"weak\":\n",
    "#     If sc_orient = 0:\n",
    "#         subset gt1r, gt2r, gt3r\n",
    "#     elif sc_orient = 1:\n",
    "#         subset gt1l, gt2l, gt3l\n",
    "#     else:\n",
    "#         print(\"Error: transition phase\"), or other output?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "df5057ba-3f8f-409b-85bd-1e3f7b24f753",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_weak_beams(f, sc_orient):\n",
    "    if sc_orient == 0:\n",
    "        # subset gt1r, gt2r, gt3r\n",
    "        track = 'gt3r' # or 'gt1r', 'gt2r'\n",
    "        weak = pd.DataFrame(data={'lat': f[track + '/sea_ice_segments/latitude'][:],\n",
    "                                  'lon': f[track + '/sea_ice_segments/longitude'][:],\n",
    "                                  'elev': f[track + '/sea_ice_segments/heights/height_segment_height'][:]\n",
    "                                 }\n",
    "                           )\n",
    "    elif sc_orient == 1:\n",
    "        # subset gt1l, gt2l, gt3l\n",
    "        track = 'gt3l' # or 'gt1l', 'gt2l'\n",
    "        weak = pd.DataFrame(data={'lat': f[track + '/sea_ice_segments/latitude'][:],\n",
    "                                  'lon': f[track + '/sea_ice_segments/longitude'][:],\n",
    "                                  'elev': f[track + '/sea_ice_segments/heights/height_segment_height'][:]\n",
    "                                 }\n",
    "                           )\n",
    "    else:\n",
    "        print(\"Error: transition phase\") # or other output?\n",
    "        weak = pd.DataFrame()\n",
    "    return weak"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59f82e88-ef2d-466c-93fd-5495e627fc59",
   "metadata": {},
   "source": [
    "Sample data to demonstrate idea (Attached in GitHub repo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7f02b1c0-5afb-455d-a500-789911d6d4f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "is2_file = 'processed_ATL07-01_20210223050453_09351001_005_01.h5'\n",
    "with h5py.File(is2_file, 'r') as f:\n",
    "    is2_gt3r = pd.DataFrame(data={'lat': f['gt3r/sea_ice_segments/latitude'][:],\n",
    "                                  'lon': f['gt3r/sea_ice_segments/longitude'][:],\n",
    "                                  'elev': f['gt3r/sea_ice_segments/heights/height_segment_height'][:]\n",
    "                                 }\n",
    "                           ) \n",
    "    is2_gt3l = pd.DataFrame(data={'lat': f['gt3l/sea_ice_segments/latitude'][:],\n",
    "                                  'lon': f['gt3l/sea_ice_segments/longitude'][:],\n",
    "                                  'elev': f['gt3l/sea_ice_segments/heights/height_segment_height'][:]\n",
    "                                 }\n",
    "                           ) \n",
    "    # orient = pd.DataFrame(data={'lat': f['gt3r/sea_ice_segments/latitude'][:],\n",
    "    #                             'lon': f['gt3r/sea_ice_segments/longitude'][:],\n",
    "    #                             'orient': f['orbit_info/sc_orient'][:]\n",
    "    #                            }\n",
    "    #                      )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7bb0262-394e-4c98-b92b-e9a4a51a2544",
   "metadata": {},
   "source": [
    "Read in the sample ATL07 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "37ccbf56-772c-4dd3-85ee-0ca99164994b",
   "metadata": {},
   "outputs": [],
   "source": [
    "is2_file = 'processed_ATL07-01_20210223050453_09351001_005_01.h5'\n",
    "f = h5py.File(is2_file, 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "890ff2a9-2bca-4c16-b2b8-e0d96bc3e8ec",
   "metadata": {},
   "source": [
    "This sample data I am using only contains gt3l and gt3r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "79b4d9f7-c417-408b-96d9-1ff5a069abff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['METADATA', 'ancillary_data', 'gt3l', 'gt3r', 'orbit_info']"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(f.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7db34f0-ec0f-4c47-a39a-af72488966bf",
   "metadata": {},
   "source": [
    "Check the value of sc_orient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "4ac4e894-0d3c-4267-a072-b89b217cba17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int8)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orient = f['orbit_info/sc_orient'][:]\n",
    "orient"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24ea2d45-7c50-48ac-b5e1-f143c8098e43",
   "metadata": {},
   "source": [
    "Extract the strong beam only from the sample ATL07 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "3db40f68-7ad9-4eb8-b483-87865eb30ea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>elev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68.232699</td>\n",
       "      <td>-109.948477</td>\n",
       "      <td>3.402823e+38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.232699</td>\n",
       "      <td>-109.948477</td>\n",
       "      <td>3.402823e+38</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.232699</td>\n",
       "      <td>-109.948477</td>\n",
       "      <td>3.402823e+38</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68.232699</td>\n",
       "      <td>-109.948477</td>\n",
       "      <td>3.402823e+38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68.232699</td>\n",
       "      <td>-109.948477</td>\n",
       "      <td>3.402823e+38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>68.393208</td>\n",
       "      <td>-109.999914</td>\n",
       "      <td>8.459367e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>68.393277</td>\n",
       "      <td>-109.999936</td>\n",
       "      <td>1.203090e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>68.393331</td>\n",
       "      <td>-109.999953</td>\n",
       "      <td>2.008147e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>68.393381</td>\n",
       "      <td>-109.999969</td>\n",
       "      <td>2.659971e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>68.393436</td>\n",
       "      <td>-109.999986</td>\n",
       "      <td>2.558262e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2028 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            lat         lon          elev\n",
       "0     68.232699 -109.948477  3.402823e+38\n",
       "1     68.232699 -109.948477  3.402823e+38\n",
       "2     68.232699 -109.948477  3.402823e+38\n",
       "3     68.232699 -109.948477  3.402823e+38\n",
       "4     68.232699 -109.948477  3.402823e+38\n",
       "...         ...         ...           ...\n",
       "2023  68.393208 -109.999914  8.459367e-02\n",
       "2024  68.393277 -109.999936  1.203090e-01\n",
       "2025  68.393331 -109.999953  2.008147e-01\n",
       "2026  68.393381 -109.999969  2.659971e-01\n",
       "2027  68.393436 -109.999986  2.558262e-01\n",
       "\n",
       "[2028 rows x 3 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "which_beam(f, orient, 'strong')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c6d4fd0c-3723-473f-80fb-c9cd12346157",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>elev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>68.232699</td>\n",
       "      <td>-109.948477</td>\n",
       "      <td>3.402823e+38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.232699</td>\n",
       "      <td>-109.948477</td>\n",
       "      <td>3.402823e+38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.232699</td>\n",
       "      <td>-109.948477</td>\n",
       "      <td>3.402823e+38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68.232699</td>\n",
       "      <td>-109.948477</td>\n",
       "      <td>3.402823e+38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68.232699</td>\n",
       "      <td>-109.948477</td>\n",
       "      <td>3.402823e+38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>68.393208</td>\n",
       "      <td>-109.999914</td>\n",
       "      <td>8.459367e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>68.393277</td>\n",
       "      <td>-109.999936</td>\n",
       "      <td>1.203090e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>68.393331</td>\n",
       "      <td>-109.999953</td>\n",
       "      <td>2.008147e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>68.393381</td>\n",
       "      <td>-109.999969</td>\n",
       "      <td>2.659971e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>68.393436</td>\n",
       "      <td>-109.999986</td>\n",
       "      <td>2.558262e-01</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "            lat         lon          elev\n",
       "0     68.232699 -109.948477  3.402823e+38\n",
       "1     68.232699 -109.948477  3.402823e+38\n",
       "2     68.232699 -109.948477  3.402823e+38\n",
       "3     68.232699 -109.948477  3.402823e+38\n",
       "4     68.232699 -109.948477  3.402823e+38\n",
       "...         ...         ...           ...\n",
       "2023  68.393208 -109.999914  8.459367e-02\n",
       "2024  68.393277 -109.999936  1.203090e-01\n",
       "2025  68.393331 -109.999953  2.008147e-01\n",
       "2026  68.393381 -109.999969  2.659971e-01\n",
       "2027  68.393436 -109.999986  2.558262e-01\n",
       "\n",
       "[2028 rows x 3 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# When orient==1, ICESat-2 is forward and the Right beams are strong\n",
    "# Check if gt3r is the output of which_beam(f,orient,'strong')\n",
    "is2_gt3r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fcd6b72-4005-43bb-9d6c-f4f9626ff5bf",
   "metadata": {},
   "source": [
    "Extract the weak beam only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "3600b376-a556-45b0-975b-826a0b453bfc",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
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       "      <th>0</th>\n",
       "      <td>68.233393</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>68.233393</td>\n",
       "      <td>-109.950679</td>\n",
       "      <td>0.199898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68.233393</td>\n",
       "      <td>-109.950679</td>\n",
       "      <td>0.199710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68.233393</td>\n",
       "      <td>-109.950679</td>\n",
       "      <td>0.199710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>68.387299</td>\n",
       "      <td>-109.999908</td>\n",
       "      <td>0.009291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>68.387356</td>\n",
       "      <td>-109.999927</td>\n",
       "      <td>0.019338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>68.387408</td>\n",
       "      <td>-109.999944</td>\n",
       "      <td>0.033115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>68.387487</td>\n",
       "      <td>-109.999971</td>\n",
       "      <td>0.051873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>68.387547</td>\n",
       "      <td>-109.999991</td>\n",
       "      <td>0.068151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1917 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            lat         lon      elev\n",
       "0     68.233393 -109.950679  0.199710\n",
       "1     68.233393 -109.950679  0.199710\n",
       "2     68.233393 -109.950679  0.199898\n",
       "3     68.233393 -109.950679  0.199710\n",
       "4     68.233393 -109.950679  0.199710\n",
       "...         ...         ...       ...\n",
       "1912  68.387299 -109.999908  0.009291\n",
       "1913  68.387356 -109.999927  0.019338\n",
       "1914  68.387408 -109.999944  0.033115\n",
       "1915  68.387487 -109.999971  0.051873\n",
       "1916  68.387547 -109.999991  0.068151\n",
       "\n",
       "[1917 rows x 3 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "which_beam(f, orient, 'weak')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91793826-e3f1-48a9-9a38-396133157e02",
   "metadata": {},
   "source": [
    "Plotting strong and weak beams to compare\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "24ab7c11-82dd-43bd-ad2c-8425a7f8814d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "which_beam(f, orient, 'weak')['elev'].plot(color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "da349bb9-58eb-4343-bb48-c949617d342f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEDCAYAAAAlRP8qAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAT+UlEQVR4nO3df6xk5X3f8fcnuwuOY6fU2dt4tfxY7NA02C2G3C4QNw6ibguUhqYlFVYUkOtqhYUlW06kuHFlp+1fcRRLxeuw3coIUzl2nNhxttHSBFVOgcoQX7a7wHrtsP6RsGULN1hmTUBO1/n2jzlzPcyde2dmd+7MPZP3SxrdM+c8c86XM5fPnnue55yTqkKS1H7fN+sCJEmTYaBL0pww0CVpThjokjQnDHRJmhMGuiTNiZkGepK7kzyb5IkR2t6e5PEkh5M8lOTSnmUfSnI0ybEkdybJxlYuSZvPrI/Q7wGuG7Htb1bV362qNwEfAj4MkOQngDcDfw94I/D3gZ+aeKWStMnNNNCr6gHgm73zkrw+yX9P8miSB5P8nabtqZ5mPwB0r4gq4BXAOcC5wDbgmQ0vXpI2ma2zLmCA/cDtVfVkkiuB3wCuBUhyB/BeOuF9LUBVfSHJ54GTQIC9VXVsJpVL0gzN+pTLyyR5FfATwG8nOQz8Z2BHd3lVfbSqXg/8EvDvms/8CPBjwPnATuDaJG+ZcumSNHOb7Qj9+4BvNefJ1/Mp4K5m+meAh6vqBYAk9wFXAQ9sVJGStBltqiP05jz515P8LEA6LmumL+lp+k+BJ5vpPwN+KsnWJNvodIh6ykXSXzuzHrb4SeALwI8mOZHkHcDPAe9IcgQ4CtzUNH9XMzTxMJ3z6Lc1838H+CrwOHAEOFJV/22K/xmStCnE2+dK0nzYVKdcJElnbmadotu3b69du3bNavOS1EqPPvron1fVwqBlQwM9ySvojBg5t2n/O1X1wb421wC/B3y9mfXZqvoP6613165dLC0tDS1ekvQ9Sf50rWWjHKF/B7i2ql5oRpE8lOS+qnq4r92DVXXj2RQqSTpzQwO9Or2mLzRvtzUve1IlaZMZqVM0yZZmuOCzwP1V9ciAZlcnOZLkviRvmGSRkqThRgr0qvpuc/Xm+cDuJG/sa3IIuKiqLgM+Anxu0HqS7EmylGRpeXn5zKuWJK0y1rDFqvoW8Ef03fK2qk51L72vqoPAtiTbB3x+f1UtVtXiwsLATlpJ0hkaGuhJFpKc10x/P/BW4Mt9bV7bfahEkt3Nep+beLWSpDWNMsplB/DxJFvoBPWnq+r3k9wOUFX7gJuBdyY5DbwE3FJegipJUzXKKJfHgMsHzN/XM70X2DvZ0tbwrSfgzz4Nr7wQfuTfTGWTktQGm+32ucOdOgZP/MfO9IU3wznnzbQcSdos2ncvlwt/Fn78P3Wm669mW4skbSLtC3RJ0kAGuiTNCQNdkuZEywPdkZGS1NXSQM+sC5CkTaelgS5J6megS9KcMNAlaU60O9C9XYwkrWhpoNspKkn9WhrokqR+BrokzQkDXZLmRMsD3U5RSepqZ6DHTlFJ6tfOQJckrWKgS9KcMNAlaU4MDfQkr0jyx0mOJDma5N8PaJMkdyY5nuSxJFdsTLn97BSVpK5RHhL9HeDaqnohyTbgoST3VdXDPW2uBy5pXlcCdzU/JUlTMvQIvTpeaN5ua179h8Y3Afc2bR8GzkuyY7Kl9nKUiyT1G+kcepItSQ4DzwL3V9UjfU12Ak/1vD/RzJMkTclIgV5V362qNwHnA7uTvLGvyaBD5lUnuJPsSbKUZGl5eXnsYiVJaxtrlEtVfQv4I+C6vkUngAt63p8PPD3g8/urarGqFhcWFsarVJK0rlFGuSwkOa+Z/n7grcCX+5odAG5tRrtcBTxfVScnXewq3g9dklaMMsplB/DxJFvo/APw6ar6/SS3A1TVPuAgcANwHHgRePsG1dvhpf+StMrQQK+qx4DLB8zf1zNdwB2TLU2SNA6vFJWkOWGgS9KcaHmg2ykqSV0tDXQ7RSWpX0sDXZLUz0CXpDlhoEvSnGh5oNspKkldLQ10O0UlqV9LA12S1M9Al6Q5YaBL0pxod6B7+1xJWtHOQPf2uZK0SjsDXZK0ioEuSXPCQJekOdHyQLdTVJK6Wh7okqSulga6o1wkqV9LA12S1G9ooCe5IMnnkxxLcjTJuwe0uSbJ80kON68PbEy5kqS1bB2hzWngF6rqUJJXA48mub+qvtTX7sGqunHyJUqSRjH0CL2qTlbVoWb628AxYOdGFzYaR7lIUtdY59CT7AIuBx4ZsPjqJEeS3JfkDWt8fk+SpSRLy8vL41f7vTWdxWclaT6NHOhJXgV8BnhPVZ3qW3wIuKiqLgM+Anxu0Dqqan9VLVbV4sLCwhmWLEkaZKRAT7KNTph/oqo+27+8qk5V1QvN9EFgW5LtE61UkrSuUUa5BPgYcKyqPrxGm9c27Uiyu1nvc5MsVJK0vlFGubwZ+Hng8SSHm3m/DFwIUFX7gJuBdyY5DbwE3FI1hZuVez90SVoxNNCr6iGG9EJW1V5g76SKGsr7oUvSKl4pKklzwkCXpDlhoEvSnGh5oNspKkldLQ10O0UlqV9LA12S1M9Al6Q5YaBL0pxoeaDbKSpJXS0NdDtFJalfSwNdktTPQJekOWGgS9KcaHege/tcSVrR7kCXJK1oZ6B7P3RJWqWdgS5JWsVAl6Q50fJAt1NUkrpaHuiSpK6hgZ7kgiSfT3IsydEk7x7QJknuTHI8yWNJrtiYcle2uLGrl6QW2jpCm9PAL1TVoSSvBh5Ncn9VfamnzfXAJc3rSuCu5qckaUqGHqFX1cmqOtRMfxs4Buzsa3YTcG91PAycl2THxKuVJK1prHPoSXYBlwOP9C3aCTzV8/4Eq0OfJHuSLCVZWl5eHrNUSdJ6Rg70JK8CPgO8p6pO9S8e8JFVQ1Cqan9VLVbV4sLCwniVDuQoF0nqGinQk2yjE+afqKrPDmhyArig5/35wNNnX96aFW3cqiWppUYZ5RLgY8CxqvrwGs0OALc2o12uAp6vqpMTrFOSNMQoo1zeDPw88HiSw828XwYuBKiqfcBB4AbgOPAi8PaJVypJWtfQQK+qhxhyjqOqCrhjUkVJksbX7itFvR+6JK1oZ6B7+1xJWqWdgS5JWsVAl6Q5YaBL0pxoeaDbKSpJXS0NdDtFJalfSwNdktTPQJekOWGgS9KcaHege6WoJK1od6BLkla0NNAd5SJJ/Voa6JKkfga6JM2Jlge6naKS1NXyQJckdbUz0L0fuiSt0s5AlyStYqBL0pwYGuhJ7k7ybJIn1lh+TZLnkxxuXh+YfJmSpGG2jtDmHmAvcO86bR6sqhsnUtFYHOUiSV1Dj9Cr6gHgm1OoZQx2ikpSv0mdQ786yZEk9yV5w1qNkuxJspRkaXl5eUKbliTBZAL9EHBRVV0GfAT43FoNq2p/VS1W1eLCwsIENi1J6jrrQK+qU1X1QjN9ENiWZPtZVyZJGstZB3qS1yadK32S7G7W+dzZrnck3g9dklYMHeWS5JPANcD2JCeADwLbAKpqH3Az8M4kp4GXgFuqNjpp7RSVpH5DA72q3jZk+V46wxolSTPklaKSNCcMdEmaEy0PdDtFJamrnYHu7XMlaZV2BrokaRUDXZLmhIEuSXOi5YFup6gkdbU80CVJXS0NdEe5SFK/lga6JKmfgS5Jc6Ldge7tcyVpRbsDXZK0op2B7qX/krRKOwNdkrSKgS5Jc6LlgW6nqCR1tTzQJUldLQ10O0Ulqd/QQE9yd5JnkzyxxvIkuTPJ8SSPJbli8mVKkoYZ5Qj9HuC6dZZfD1zSvPYAd519WZKkcQ0N9Kp6APjmOk1uAu6tjoeB85LsmFSBkqTRTOIc+k7gqZ73J5p5qyTZk2QpydLy8vIENu0oF0nqmkSgD+qhHJi0VbW/qharanFhYWHCm5Skv94mEegngAt63p8PPD2B9UqSxjCJQD8A3NqMdrkKeL6qTk5gvZKkMWwd1iDJJ4FrgO1JTgAfBLYBVNU+4CBwA3AceBF4+0YVK0la29BAr6q3DVlewB0Tq2gc3g9dkla080pRb58rSau0M9AlSasY6JI0Jwx0SZoTLQ90O0UlqavlgS5J6mppoDvKRZL6tTTQJUn9DHRJmhMtD3Q7RSWpq+WBLknqammg2ykqSf1aGuiSpH4GuiTNiXYHurfPlaQV7Q50SdKKdga690OXpFXaGeiSpFUMdEmaEwa6JM2JkQI9yXVJvpLkeJL3DVh+TZLnkxxuXh+YfKmDOMpFkrq2DmuQZAvwUeAfASeALyY5UFVf6mv6YFXduAE1DqpqOpuRpBYZ5Qh9N3C8qr5WVX8JfAq4aWPLkiSNa5RA3wk81fP+RDOv39VJjiS5L8kbBq0oyZ4kS0mWlpeXz6BcSdJaRgn0Qec3+k9eHwIuqqrLgI8Anxu0oqraX1WLVbW4sLAwVqGSpPWNEugngAt63p8PPN3boKpOVdULzfRBYFuS7ROrck12ikpS1yiB/kXgkiQXJzkHuAU40NsgyWuTzuWbSXY3631u0sX2bHHjVi1JLTV0lEtVnU7yLuAPgC3A3VV1NMntzfJ9wM3AO5OcBl4CbqnyzlmSNE1DAx1WTqMc7Ju3r2d6L7B3sqVJksbhlaKSNCfaHeie1ZGkFe0OdEnSinYGuvdDl6RV2hnokqRVDHRJmhMtD3Q7RSWpq+WBLknqammg2ykqSf1aGuiSpH4GuiTNiXYHuleKStKKdge6JGlFSwPdTlFJ6tfSQJck9TPQJWlOtDzQ7RSVpK6WB7okqaudge7tcyVplXYGuiRplZECPcl1Sb6S5HiS9w1YniR3NssfS3LF5EuVJK1naKAn2QJ8FLgeuBR4W5JL+5pdD1zSvPYAd024TknSEFtHaLMbOF5VXwNI8ingJuBLPW1uAu6tqgIeTnJekh1VdXLiFff6X7fA1ldu6CYkaeJe9w74sfdOfLWjBPpO4Kme9yeAK0dosxN4WaAn2UPnCJ4LL7xw3Fq/54euhItvg9N/cebrkKRZecUPb8hqRwn0QUNK+geAj9KGqtoP7AdYXFw880Hk574Grr7njD8uSfNolE7RE8AFPe/PB54+gzaSpA00SqB/EbgkycVJzgFuAQ70tTkA3NqMdrkKeH7Dz59Lkl5m6CmXqjqd5F3AHwBbgLur6miS25vl+4CDwA3AceBF4O0bV7IkaZBRzqFTVQfphHbvvH090wXcMdnSJEnj8EpRSZoTBrokzQkDXZLmhIEuSXMinf7MGWw4WQb+9Aw/vh348wmWMymbsa7NWBNY1zg2Y02wOevajDXBZOu6qKoWBi2YWaCfjSRLVbU46zr6bca6NmNNYF3j2Iw1weasazPWBNOry1MukjQnDHRJmhNtDfT9sy5gDZuxrs1YE1jXODZjTbA569qMNcGU6mrlOXRJ0mptPUKXJPUx0CVpTrQu0Ic9sHoDt3tBks8nOZbkaJJ3N/N/Jcn/SXK4ed3Q85l/29T5lST/ZANr+0aSx5vtLzXzXpPk/iRPNj//5rTqSvKjPfvjcJJTSd4zi32V5O4kzyZ5omfe2PsmyY83+/h480D0QQ91OZuafi3Jl5uHrP9ukvOa+buSvNSzz/b1fGZiNa1T19jf2ZTq+q2emr6R5HAzfyr7a508mOnvFlXVmhed2/d+FXgdcA5wBLh0StveAVzRTL8a+BM6D83+FeAXB7S/tKnvXODipu4tG1TbN4DtffM+BLyvmX4f8KvTrqvnO/u/wEWz2FfAW4ArgCfOZt8AfwxcTefpXPcB10+4pn8MbG2mf7Wnpl297frWM7Ga1qlr7O9sGnX1Lf914APT3F+snQcz/d1q2xH6ygOrq+ovge4DqzdcVZ2sqkPN9LeBY3Sem7qWm4BPVdV3qurrdO4Vv3vjK33Z9j/eTH8c+OczqusfAl+tqvWuCt6wmqrqAeCbA7Y38r5JsgP4war6QnX+D7y35zMTqamq/rCqTjdvH6bz1K81Tbqmtepax1T21bC6mqPZfwV8cr11bMB3uFYezPR3q22BvtbDqKcqyS7gcuCRZta7mj+V7+75E2uatRbwh0keTedB3AA/XM1To5qff2sGdUHnCVe9/7PNel/B+PtmZzM9rfr+NZ0jta6Lk/zvJP8zyU/21Dqtmsb5zqa9r34SeKaqnuyZN9X91ZcHM/3dalugj/Qw6g0tIHkV8BngPVV1CrgLeD3wJuAknT//YLq1vrmqrgCuB+5I8pZ12k6trnQeWfjTwG83szbDvlrPWnVMc5+9HzgNfKKZdRK4sKouB94L/GaSH5xiTeN+Z9P+Lt/Gyw8Yprq/BuTBmk3X2P5E62pboM/0YdRJttH58j5RVZ8FqKpnquq7VfVXwH/he6cKplZrVT3d/HwW+N2mhmeaP+e6f24+O+266PwDc6iqnmnqm/m+aoy7b07w8lMgG1JfktuAG4Gfa/78pvkT/blm+lE6517/9rRqOoPvbCp1ASTZCvwL4Ld66p3a/hqUB8z4d6ttgT7KA6s3RHOu7mPAsar6cM/8HT3Nfgbo9sQfAG5Jcm6Si4FL6HR+TLquH0jy6u40nc61J5rt39Y0uw34vWnW1XjZ0dOs91WPsfZN86fzt5Nc1fwe3NrzmYlIch3wS8BPV9WLPfMXkmxppl/X1PS1adTUbHOs72xadTXeCny5qlZOWUxrf62VB8z6d+tMe1Nn9aLzMOo/ofMv7/unuN1/QOdPoceAw83rBuC/Ao838w8AO3o+8/6mzq9wlj3969T1Ojq950eAo919AvwQ8D+AJ5ufr5lyXa8EngP+Rs+8qe8rOv+gnAT+H52joXecyb4BFumE2VeBvTRXWU+wpuN0zrF2f7f2NW3/ZfO9HgEOAf9sI2pap66xv7Np1NXMvwe4va/tVPYXa+fBTH+3vPRfkuZE2065SJLWYKBL0pww0CVpThjokjQnDHRJmhMGuiTNCQNdkubE/wc2LFK3uYyJ5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "which_beam(f, orient, 'strong')['elev'].plot(color='orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6989c86f-1865-46eb-a819-3f0c2e779e5f",
   "metadata": {},
   "source": [
    "Looks like the strong beam has some invalid elevation values. Let's exclude them and plot again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "5fe52214-41c1-4b93-a6e1-f42e5ea55b28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "elev = which_beam(f, orient, 'strong')['elev']\n",
    "elev[elev<1^38].plot(color='orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db0574b6-cc3d-4c85-83f5-b60ee4a920ed",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}