SDBProduction_FromSamplePointCloudToDepth.ipynb

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      "name": "SDBProduction_FromSamplePointCloudToDepth.ipynb",
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      "name": "python"
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      "cell_type": "markdown",
      "source": [
        "# **Satellite Derived Bathymetry with sample elevation data by Google Earth Engine**\n",
        "\n",
        "การทำแบบจำลองความลึกท้องน้ำตื้นโดยใช้ภาพถ่ายดาวเทียม Sentinel 2 (L2A : Bottom of the atmosphere) แปลงค่าการสะท้อนในช่วงคลื่นสีน้ำเงินและเขียวเป็นความลึกของน้ำผ่านสมการเชิงประจักษ์ (Empirical formula) สำหรับพื้นที่ชายฝั่งทะเล\n",
        "\n",
        "\n",
        "**ต้องการข้อมูลค่ารังวัดภาคสนามประกอบการทำแบบจำลอง**\n",
        "\n",
        "\n",
        "Thepchai Srinoi and Associate Professor Dr. Phisan Santitamnont\n",
        "\n",
        "Department of Survey Engineering\n",
        "\n",
        "Faculty of Engineering\n",
        "\n",
        "Chulalongkorn University"
      ],
      "metadata": {
        "id": "6b47mzd8_6ZO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prepared your library ...."
      ],
      "metadata": {
        "id": "r5DU65RHAHh8"
      }
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    {
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        "id": "y0nD2oNkjm_6",
        "outputId": "f61bb089-eb62-48ab-b735-3888fe68c0c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pygeodesy\n",
            "  Downloading PyGeodesy-22.6.9-py2.py3-none-any.whl (796 kB)\n",
            "\u001b[K     |████████████████████████████████| 796 kB 6.7 MB/s \n",
            "\u001b[?25hInstalling collected packages: pygeodesy\n",
            "Successfully installed pygeodesy-22.6.9\n"
          ]
        }
      ],
      "source": [
        "!pip install pygeodesy"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import ee\n",
        "# Authenticate to the Earth Engine servers\n",
        "ee.Authenticate()\n",
        "# Initialize the API\n",
        "ee.Initialize()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2b7ZknlQjumR",
        "outputId": "90b95dba-2a20-45fc-8a29-25d79a771ac5"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "To authorize access needed by Earth Engine, open the following URL in a web browser and follow the instructions. If the web browser does not start automatically, please manually browse the URL below.\n",
            "\n",
            "    https://code.earthengine.google.com/client-auth?scopes=https%3A//www.googleapis.com/auth/earthengine%20https%3A//www.googleapis.com/auth/devstorage.full_control&request_id=fSGRvjXM_W_s8UGuakMul2jJ4zXhUJ2oM8Mfb7YXN2o&tc=eTPEydf3-MNueLbkYye8VMHKpLnsKD1KxwLATYsRR74&cc=KHv5MQvfCdIapmEtLTXvHT8F24uGf0iYAy-PWk3ncCs\n",
            "\n",
            "The authorization workflow will generate a code, which you should paste in the box below. \n",
            "Enter verification code: 4/1AX4XfWhd7x8bSaqT0HD1Oi8v_CCoYATkENk3TRLxxQ0CGaldzuLi6e91tzA\n",
            "\n",
            "Successfully saved authorization token.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import the crucial libraries\n",
        "import pandas as pd\n",
        "import matplotlib as mp\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import folium\n",
        "import pygeodesy\n",
        "from pygeodesy.ellipsoidalKarney import LatLon"
      ],
      "metadata": {
        "id": "g7TeeJjzj6bl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def photonplot(X,Y) :\n",
        "  plt.title('photon profile')\n",
        "  plt.ylabel('ellipsoidal height (m)')\n",
        "  plt.xlabel('latitude (deg)')\n",
        "  #plt.scatter(df_train_select.lat,df_train_select.h)\n",
        "  plt.scatter(X,Y)\n",
        "  plt.grid()\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "xX-FD2aAnvf5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import your sample point cloud from field survey ... (photon height -- ellipsoidal height)\n",
        "\n",
        "คำเตือน : ค่าระดับจาก ICESat-2 https://openaltimetry.org/data/icesat2/ ต้องทำ refraction correction ก่อน เพื่อไม่ให้แบบจำลองความลึกของเราลึกเกิน"
      ],
      "metadata": {
        "id": "jvX8c2xQALnR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('/content/drive/MyDrive/SeniorProject/myinput/sdb_icesat2_csv/photon_2022-02-06_gt1l_t705_1655282450907.csv')\n",
        "df"
      ],
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        "id": "am-eyd-vkAXu",
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      "execution_count": null,
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              "        latitude   longitude  photon height  confidence code\n",
              "0      12.687742  101.385779     280.057740                0\n",
              "1      12.687730  101.385801    -132.808790                0\n",
              "2      12.687727  101.385794      -5.886936                4\n",
              "3      12.687722  101.385804    -190.066100                0\n",
              "4      12.687721  101.385793      -5.655722                4\n",
              "...          ...         ...            ...              ...\n",
              "84590  12.465319  101.363125     -25.070978                4\n",
              "84591  12.465312  101.363124     -25.184513                4\n",
              "84592  12.465306  101.363123     -25.041723                4\n",
              "84593  12.465299  101.363123     -25.007881                4\n",
              "84594  12.465293  101.363122     -25.138102                4\n",
              "\n",
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        "Df = df.copy()\n",
        "#Df = Df.loc[Df['confidence code'] == 4]\n",
        "Df = Df.loc[(Df['latitude'] > 12.61) & (Df['latitude'] < 12.616)]\n",
        "Df = Df.loc[(Df['photon height'] > -40) & (Df['photon height'] < -20) ]\n",
        "plt.scatter(Df['latitude'], Df['photon height'])"
      ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Df = df.copy()\n",
        "Df = Df.loc[Df['confidence code'] == 4]\n",
        "Df = Df.loc[(Df['latitude'] > 12.609) & (Df['latitude'] < 12.615)]\n",
        "Df = Df.loc[(Df['photon height'] > -40) & (Df['photon height'] < -20) ]\n",
        "plt.scatter(Df['latitude'], Df['photon height'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "9hDIZMmBkXLG",
        "outputId": "2bf711a0-9b57-4463-eb87-cc41b58691dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f56d2559b10>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Df = df.copy()\n",
        "Df = Df.loc[Df['confidence code'] == 4]\n",
        "Df = Df.loc[(Df['latitude'] > 12.614) & (Df['latitude'] < 12.61475)]\n",
        "Df = Df.loc[(Df['photon height'] > -26) & (Df['photon height'] < -20) ]\n",
        "plt.scatter(Df['latitude'], Df['photon height'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "LwHq8IdQnKIx",
        "outputId": "eff88e48-76b2-4755-9d81-a82845deda44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f56d1dc16d0>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Select the height of sea floor point cloud (Assume mean sea level H = 0)"
      ],
      "metadata": {
        "id": "pggUqi57AcsC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "S1 = (df.loc[(df['latitude'] > 12.609) & (df['latitude'] < 12.615) & (df['confidence code'] == 4)\n",
        "                & (df['photon height'] > -40) & (df['photon height'] < -27)]).copy()\n",
        "\n",
        "S2 = (df.loc[(df['latitude'] > 12.614) & (df['latitude'] < 12.615) & (df['confidence code'] == 4)\n",
        "                & (df['photon height'] > -27) & (df['photon height'] < -26)]).copy()\n",
        "\n",
        "S3 = (df.loc[(df['latitude'] > 12.614) & (df['latitude'] < 12.61475) & (df['confidence code'] == 4)\n",
        "                & (df['photon height'] > -26) & (df['photon height'] < -25.70)]).copy()\n",
        "\n",
        "trainphoton = pd.concat([S1,S2,S3])\n",
        "print('Count', trainphoton['photon height'].count())\n",
        "\n",
        "photonplot(trainphoton['latitude'],trainphoton['photon height'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "daaKKVkJn3Rk",
        "outputId": "a906c9a3-029e-474b-cd8e-18b9b58c81c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Count 97\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install Function for Cloud Mask\n",
        "def maskS2clouds(image) :\n",
        "  qa = image.select('QA60')\n",
        "\n",
        "  # Bits 10 and 11 are clouds and cirrus, respectively.\n",
        "  cloudBitMask = 1 << 10\n",
        "  cirrusBitMask = 1 << 11\n",
        "\n",
        "  # Both flags should be set to zero, indicating clear conditions.\n",
        "  mask = (qa.bitwiseAnd(cloudBitMask).eq(0)) and (qa.bitwiseAnd(cirrusBitMask).eq(0))\n",
        "\n",
        "  return image.updateMask(mask).divide(10000)\n",
        "\n",
        "# Map Visualization : Google Earth Engine\n",
        "def add_ee_layer(self, ee_image_object, vis_params, name):\n",
        "  map_id_dict = ee.Image(ee_image_object).getMapId(vis_params)\n",
        "  folium.raster_layers.TileLayer(\n",
        "      tiles=map_id_dict['tile_fetcher'].url_format,\n",
        "      attr='Map Data &copy; <a href=\"https://earthengine.google.com/\">Google Earth Engine</a>',\n",
        "      name=name,\n",
        "      overlay=True,\n",
        "      control=True\n",
        "  ).add_to(self)"
      ],
      "metadata": {
        "id": "CNj99DBarUbM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "prepare your satellite image"
      ],
      "metadata": {
        "id": "UytYMtcBA3uV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "############# PREPARED DATA NAJA #############################\n",
        "# PART 5 SDB FOR MY METHOD 13.450553 13.450553 100.229162 100.231138\n",
        "lat_min, lat_max, lon_min, lon_max = (12.507892, 12.652677, 101.265584, 101.549246)\n",
        "AOI = ee.Geometry.Polygon(\n",
        "        [[[lon_min, lat_max],\n",
        "           [lon_min, lat_min],\n",
        "           [lon_max, lat_min],\n",
        "           [lon_max, lat_max]]])\n",
        "fromdate = '2021-12-30'\n",
        "todate = '2022-6-15'\n",
        "\n",
        "image = ee.ImageCollection('COPERNICUS/S2_SR').filterDate(fromdate,todate).\\\n",
        "          filterMetadata('CLOUDY_PIXEL_PERCENTAGE','less_than',0.5).\\\n",
        "          filterBounds(AOI).map(maskS2clouds).first().clip(AOI)\n",
        "\n",
        "if 1 :\n",
        "  B8_threshold = 0.1\n",
        "\n",
        "  B8_c = (image.select('B8').lt(B8_threshold)).selfMask()\n",
        "  B2_lm = image.select('B2').multiply(B8_c)\n",
        "  B3_lm = image.select('B3').multiply(B8_c)\n",
        "  B4_lm = image.select('B4').multiply(B8_c)\n",
        "  B8_lm = image.select('B8').multiply(B8_c)\n",
        "  image = image.addBands(B2_lm.rename('B2_lm'))\n",
        "  image = image.addBands(B3_lm.rename('B3_lm'))\n",
        "  image = image.addBands(B4_lm.rename('B4_lm'))\n",
        "  image = image.addBands(B4_lm.rename('B8_lm'))\n",
        "#------------------------------------------------------------------------------------------------\n",
        "############### VISUALIZATION ############################## \n",
        "folium.Map.add_ee_layer = add_ee_layer\n",
        "c = (AOI.centroid().getInfo())['coordinates']\n",
        "map_matched = folium.Map(location=[c[1], c[0]], zoom_start=12)\n",
        "\n",
        "depth_palette = ['blue','cyan', 'green','orange', 'red']\n",
        "\n",
        "map_matched.add_ee_layer(image,{'min':0, 'max':0.3, 'bands':['B4','B3','B2']},'RGB_L2A')\n",
        "map_matched.add_ee_layer(image,{'min':0, 'max':0.1, 'bands':['B2_lm']},'BLUE_L2A')\n",
        "\n",
        "display(map_matched.add_child(folium.LayerControl()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "IarBF3s_q5IY",
        "outputId": "a6d49338-5918-4c32-e01d-ffb21e3f518d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<folium.folium.Map at 0x7f56d1c70a50>"
            ],
            "text/html": [
              "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
              "&lt;head&gt;    \n",
              "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
              "    &lt;script&gt;L_PREFER_CANVAS=false; L_NO_TOUCH=false; L_DISABLE_3D=false;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css&quot;/&gt;\n",
              "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
              "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
              "    \n",
              "    &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
              "        initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
              "    &lt;style&gt;#map_adcb47f72da5d0222fae8868882d9362 {\n",
              "        position: relative;\n",
              "        width: 100.0%;\n",
              "        height: 100.0%;\n",
              "        left: 0.0%;\n",
              "        top: 0.0%;\n",
              "        }\n",
              "    &lt;/style&gt;\n",
              "&lt;/head&gt;\n",
              "&lt;body&gt;    \n",
              "    \n",
              "    &lt;div class=&quot;folium-map&quot; id=&quot;map_adcb47f72da5d0222fae8868882d9362&quot; &gt;&lt;/div&gt;\n",
              "&lt;/body&gt;\n",
              "&lt;script&gt;    \n",
              "    \n",
              "    \n",
              "        var bounds = null;\n",
              "    \n",
              "\n",
              "    var map_adcb47f72da5d0222fae8868882d9362 = L.map(\n",
              "        &#x27;map_adcb47f72da5d0222fae8868882d9362&#x27;, {\n",
              "        center: [12.580315013034324, 101.40741500000003],\n",
              "        zoom: 12,\n",
              "        maxBounds: bounds,\n",
              "        layers: [],\n",
              "        worldCopyJump: false,\n",
              "        crs: L.CRS.EPSG3857,\n",
              "        zoomControl: true,\n",
              "        });\n",
              "\n",
              "\n",
              "    \n",
              "    var tile_layer_e7af85108d31601c8b66e7ee5c3b2d8b = L.tileLayer(\n",
              "        &#x27;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: null,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 0,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_adcb47f72da5d0222fae8868882d9362);\n",
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              "        &#x27;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/fce266983d6b38031b6d1f9b0b6dcc08-e5c3832b9ddd813f36a6cb596478eed3/tiles/{z}/{x}/{y}&#x27;,\n",
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              "        &quot;attribution&quot;: &quot;Map Data &amp;copy; &lt;a href=\\&quot;https://earthengine.google.com/\\&quot;&gt;Google Earth Engine&lt;/a&gt;&quot;,\n",
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              "        &#x27;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/ed5d50694660e6c15debc9cd22ca8c71-a909b12d6dbb8f48a8db632eaec08561/tiles/{z}/{x}/{y}&#x27;,\n",
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              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 0,\n",
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              "}).addTo(map_adcb47f72da5d0222fae8868882d9362);\n",
              "    \n",
              "            var layer_control_c4e720176ba1d5842c825034a5faba81 = {\n",
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              "        \n",
              "&lt;/script&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
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    },
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      "cell_type": "code",
      "source": [
        "trainphoton"
      ],
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              "        latitude   longitude  photon height  confidence code\n",
              "32772  12.614146  101.378301     -27.703670                4\n",
              "32816  12.614064  101.378292     -27.048449                4\n",
              "32853  12.613981  101.378283     -27.056513                4\n",
              "32878  12.613949  101.378280     -27.111288                4\n",
              "32879  12.613949  101.378280     -27.111906                4\n",
              "...          ...         ...            ...              ...\n",
              "32667  12.614306  101.378318     -25.762090                4\n",
              "32671  12.614300  101.378317     -25.826885                4\n",
              "32701  12.614261  101.378313     -25.799225                4\n",
              "32766  12.614166  101.378303     -25.822392                4\n",
              "32811  12.614070  101.378293     -25.842790                4\n",
              "\n",
              "[97 rows x 4 columns]"
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "get raster value for sdb production .."
      ],
      "metadata": {
        "id": "442y1VnvBZ0m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# SDB Calculate\n",
        "nB2 = (image.select('B2_lm')).multiply(1000)\n",
        "nB3 = (image.select('B3_lm')).multiply(1000)\n",
        "lnB2perlnB3 = (nB2.log()).divide(nB3.log())\n",
        "image = image.addBands(lnB2perlnB3.rename('lnB2perlnB3'))\n",
        "\n",
        "  def getvalue(vec) :\n",
        "    lat,lng = vec\n",
        "    u_poi = ee.Geometry.Point([lng,  \tlat ])\n",
        "    band = image.select('lnB2perlnB3')\n",
        "    mydata = band.sample(u_poi, 10).getInfo()\n",
        "    mylist = (mydata)['features']\n",
        "    if mylist != [] :\n",
        "      v1 = ((mylist[0])['properties'])['lnB2perlnB3']\n",
        "    else :\n",
        "      v1 = None \n",
        "    return [v1]\n",
        "\n",
        "trainphoton['lnB2perlnB3'] = trainphoton[['latitude', 'longitude']].apply(getvalue, axis=1,  result_type ='expand')"
      ],
      "metadata": {
        "id": "_L61XCrZswtg"
      },
      "execution_count": null,
      "outputs": []
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    {
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        "id": "KT0fSm1Zwz7v",
        "outputId": "a4e42e11-92a1-402d-f68e-9aa35fb03c93"
      },
      "execution_count": null,
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          "data": {
            "text/plain": [
              "        latitude   longitude  photon height  confidence code  lnB2perlnB3\n",
              "32772  12.614146  101.378301     -27.703670                4     0.917257\n",
              "32816  12.614064  101.378292     -27.048449                4     0.914222\n",
              "32853  12.613981  101.378283     -27.056513                4     0.917256\n",
              "32878  12.613949  101.378280     -27.111288                4     0.917256\n",
              "32879  12.613949  101.378280     -27.111906                4     0.917256\n",
              "...          ...         ...            ...              ...          ...\n",
              "32667  12.614306  101.378318     -25.762090                4     0.917893\n",
              "32671  12.614300  101.378317     -25.826885                4     0.917893\n",
              "32701  12.614261  101.378313     -25.799225                4     0.917893\n",
              "32766  12.614166  101.378303     -25.822392                4     0.917257\n",
              "32811  12.614070  101.378293     -25.842790                4     0.914222\n",
              "\n",
              "[97 rows x 5 columns]"
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              "      <td>12.614166</td>\n",
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              "      <td>4</td>\n",
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              "      <td>12.614070</td>\n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-aaaaafdd-2c5e-4bf2-83ff-6e13dd60fe77 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-aaaaafdd-2c5e-4bf2-83ff-6e13dd60fe77');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainphoton = trainphoton[~trainphoton['lnB2perlnB3'].isna()]"
      ],
      "metadata": {
        "id": "5RDKbOzexstx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "photonplot(trainphoton['lnB2perlnB3'],trainphoton['photon height'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "hcnS4ae9w9wr",
        "outputId": "647eb9c0-ea1c-4b93-85a4-b056d8674fab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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4ocA9hwLl5Si0Fkaj/puYtZpiVw5fBz4raS5wJ/A4sBWwF7At8F3AiaGGJmt7z0qWcTXqv4lZqyk28d6aiPhbYH+SRPEz4ArgQxHx2oj4SkQ8V+j9VrlGnRo7y7ga9d/ErNWUcs9hCOivfSg2XjXWoW61uMZ6cOmZ+4FkadJjFlS2HKuZTeQ1pBtcpetQ10pWcY314PrI3pv34OrdfYeG/Hcya1ZODtaQ+lYPFOyt1Ig9uMxaTSlrSL+rlLKpkLRc0t2S7pB0maTutLxT0vclrZP0G0nLKjmPNaexuZUGBocJXpxbqW/1gHsrmdVJKaOW8n1AV/qhfT3wmnQq8HtyjvcuYMuImAssAP5B0h4VnsuaTLGrg3rMCGtmxcc5vA14O9Aj6as5m7YFXqjkpBFxXc7LW4F3jm0Ctpa0BdAFPA/8pZJzWfMpdnVw3IG71XRGWDNLqNB8epJeC8wDPgecmrPpaeDmiHiyKgFIVwIXRcT5kjqBHwKHADOAT0bEeQXetwRYAjBr1qwFK1asqDiWoaEhZs6cWfFxGkkz1mn9I0/z/GieRY46kgvd50c3MqsLHh3efNucnZt7Vpdm/F1NphXrBK1Tr0WLFq2KiN582womh007SJ0RMeWhr5JuAHbOs+nkiLg83edkoBc4OiJC0kHAR0iWJd2eZGzF2yLivmLn6u3tjZUrV041xAn6+/tZuHBhxcdpJM1Yp/HrOUAyluGso+fyyYvWEMCn5r7AOetevPAVcP/Z76h/sFXUjL+rybRinaB16iWpYHIopbfS6ySdDuye7j+22M/Li70pIg6dJKjjgcOBQ3KmA38v8N9pMnpM0i0kyaNocrDWkm822rGxDMuvXd+QI6QL9a4ya1al3JD+DsmCP28gGS3dm/4sm6S3Ap8BjoyIDTmbfg8cnO6zNXAgcHcl57LmU2w22kYcIV2sd5VZsyolOTwVET+JiMci4omxR4XnPZdk2u/rJa2R9M20/OvATEl3AbcB34uIOyo8lzWZycYynHX0XKZ3TENAT3cXZx09N9Nv6V6dzlpRsd5K+6VPb5a0HLiUzRf7ub3ck0bEKwuUD5F0Z7U2NtlYhsXze+h/6rfcf/bCOkZVmMdeWCsqds/hnHGvc29aBGnzj1m1NdvMq80Wr1kpis3KuqjIw4nBaqYR7ysU02zxmpVi0t5Kkv4pT/FTwKqIWFP9kKzdNepstIU0W7xmpSilK2tv+rgyfX04cAdwoqSLI+LfahWcWbNo1NlzzcpVSnLYBdgvvVmMpNOAq4E3AqsAJwerqr7VAyy9eC0jG5OurAODwyy9eC2AP4DN6qSU5PBScnopASPArIgYluSV4KzqTr/irk2JYczIxuD0K+5q2OTgQXDWakpJDhcAv5J0efr6COBH6SC1X9csMmtbg8P5Z2sZK+9bPcCjjzzNCSdd3RAfxOOn+xgbBAcTr3ScRKxZTDoILiI+TzLB3WD6ODEiPhcRz0TEcbUO0CzX2Afx86MbG2Y0cqmD4DyS2ppJweQgadv05w4kcxv9MH3cl5aZ1cSMzvx/ljM6pzXkaORSB8E1YuxmhRRrVvoRSc+kVSSD3jTuZ9GJ98zKtWVnBxtGJk7ZvWVnR0OORi51EFwjxm5WSLFBcIenP/eMiJeP/1m/EK3dDG4ocM9hw0hDrgRX6iC4RozdrJBS1pCWpPdJ+mz6ejdJr6t9aNauin2INuJo5LHJAHu6u4pOBtiIsZsVUkpvpW8AG0nmUvo8yUpwl1DhtN1mhSw9bE7exX5ye/Y8uv52BA3T46eUQXAeSW3NpJTkcEBE7CdpNUBEPClpeo3jsjY22Ydoo83KOhUeSW3NopTkMCKpg+QmNJJ2IrmSMKuZZvkQPaVv3WYr1r3ngF05c/Hcqh0/37gI8NWH1V4pyeGrwGXASyV9AXgncEpNozJrAqf0reP8W3+/6fVoxKbX1UgQ+QbXLf3xWgg2m1qk0IA7s0qUMgjuApIlPc8C/ggsjoiLax2Ytbe+1QMcdPZN7HnS1Rx09k0NOVDswl89NKXyqco3LmJkNCZMLeKxElYLpVw5APwW+MvY/pJ2i4jfF3+LWXmmMh1FlsbWuC61fKqmMv7BYyWs2krpyvpR4FHgeuAqkhlZr6pxXNbGPJI4MZXxD90zOmsYibWjUq4cPg7MiYgnah2MGZQ3krgVJ7TL16W3kGdL2MdsKia9cgAeIln5zawupjqSuFUntMs3uK6Q4TzTjZhVouCVQ87yoPcB/ZKuJmddh4j4UrknlbScZOrv54F7gRMiYjAdP/EtkpXnNgIfj4j+cs9jzanYILh8ijVD1fLqYZpgY57bC9NUvXOM79K7x0lXV+/gZkUUu3LYJn38nuR+w/Scsm0qPO/1wGsiYl/gHmBZWv73ABExF3gzcI6kUq5urIWUOh3FmKwmtHvvAbvlLd9yi2k1u2rZvsC9hULlZuUqeOUQEWfU6qQRcV3Oy1tJxk4AvBq4Kd3nMUmDJFcR/1urWKwxTWUQXKmzolbbmYvncv/jQ9xy7583Kx8e2Viz3lWnHbEPS3+8lpHRFy9ZOjvEaUfsU9XzmCmq1O2u7ACkK4GLIuJ8SUtIrhjeA+wKrAY+GBGX5HnfEpJFiJg1a9aCFStWVBzL0NAQM2fOrPg4jaQV6wSb12tweISBJ4fZmPO3PE2iZ/suurtq940633lzTe+YxpydS7/ILvV3NTg8wqNPPcvzoxuZ3jGNWdttVdN6VqId/v6a2aJFi1ZFRG++baWOc5gySTcAO+fZdHJEXJ7uczLwAslSpADfBf4KWAk8CPwCyNsNIyLOA84D6O3tjYULF1Ycc39/P9U4TiNpxTrBxHpl0VvpoLNvYmCwo+B2wZTmf2rF31Ur1glat165apYcIuLQYtslHU+ymNAhkV6+RMQLwCdz9vkFyT0Js6KymIspX1NWLq/TYM2sWG+lr5FOtpdPRHys3JNKeivJlBxviogNOeUzSJq6npH0ZuCFiPh1uecxq6UOqeBoaK/TYM2u2JXDyhqe91xgS+B6SQC3RsSJwEuBayVtBAaA99cwBrOKFJsmo1jvKrNmUKy30vdrddKIeGWB8gcAf92yptBToJdUT3eXE4M1vVLmVtpJ0hclXSPpprFHPYIza2Re9tNaWSkDzC4AfgPsCZwBPADcVsOYzJrCVAfrmTWTUnor7RgR35H08Yj4H+B/JDk5mFH7XlKtOKGgNYeSlglNf/5R0juAh4EdaheSmUH+dS0+edEaPnHRGnqcKKzGSkkOZ0raDvgU8DVgW3LGIphZbeSbUHCsf1SjLoBkrWPS5BARYwv7PAUsqm04ZjZmsokD6zHzrLWvTAbBmVVbK7bNF5pQMJeXB7VaKdZbaSWwCtgK2I9kHenfAvNIpu82awituthPvq6y43mKDquVSQfBSfow8IZ03iMkfRP4WX3CM5tcVov91NpY7MuvXV/wCmLR3jvVMyRrI6WMc9ie5Cb0mJlpmVlDyGqxn3pYPL+HW046uOASoTff/XidI7J2UUpvpbOB1ZJuJpmF+I3A6bUMymwqslrsp55aOQFaY5r0yiEivgccAFwGXAq8vpbzLplNVTtMY1Eo0bVSArTGUjA5SNo7/bkfMBt4KH3MTsvMGsLi+T0cs6CHjmSGXzokjllQ//UdamnpYXPonKbNyjqnqaUSoDWWYs1K/0SyDOc5ebYFcHBNIjKbor7VA1yyamDTFNqjEVyyaoDe3XdoqQSBJnltVkXFeistSX964Js1tFbtrZRr+bXrGRndfNjRyGi0VB2tsUx6Q1pSJ/BhkhvRAP3AtyJipOCbzOqoHW7WtkMdrbGU0pX134EFwDfSx4K0zKwhtMPN2naoozWWUpLD/hHxdxFxU/o4Adi/1oGZlaodeiu1Qx2tsZQyzmFU0isi4l4ASS8HRid5j1nd5I4kbqW5lXK1Qx2tsZSSHJYCN0u6j6R/xO7ACTWNymyKar3oTiNohzpa4yhlyu4bJe0FjF2/ro+I52oblpmZZWnSew6S3gVMj4g7gCOBCz0IzsystZVyQ/qzEfG0pDcAhwDfoQq9lSR9XtIdktZIuk7S7LRckr4q6XfpdiciM7M6KyU5jN18fgfw7Yi4muqs57A8IvaNiHnAVcCpafnbgL3SxxLcbdbMrO5KSQ4Dkr4FHAtcI2nLEt9XVET8Jefl1ry46txRwA8icSvQLelllZ7PzMxKp4iCK4EmO0gzgLcC6yLit+kH9dyIuK7ik0tfAD5Auj51RDwu6Srg7Ij4ebrPjcA/R8TKce9dQnJlwaxZsxasWLGi0nAYGhpi5syZFR+nkbRinaA16+U6NY9WqdeiRYtWRURv3o0RMemDZJnQjwEfBfYr5T3p+24A7szzOGrcfsuAM9LnV5GsPDe27Uagt9h5FixYENVw8803V+U4jaQV6xTRmvVynZpHq9QLWBkFPldLmVvpVOBdJGs5AHxP0sURceZk742IQyfbJ3UBcA1wGjAA7JqzbZe0zMzM6qSUewfHkUyhcVpEnAYcCLy/0hOnYyfGHAXcnT6/AvhA2mvpQOCpiPhjpeczM7PSlTJC+mFgK+DZ9PWWVOeb/NmS5gAbgQeBE9Pya4C3A78DNuDR2GZmdVdKcngKuEvS9SQ9it4M/K+krwJExMfKOXFEHFOgPIB/LOeYZmZWHaUkh8vSx5j+2oRiZmaNopS5lb5fj0DMzKxxFEwOkv4rIv5W0jpeHKC2SUTsW9PIzMwsM8WuHD6e/jy8HoGYmVnjKJgcxrqPRsSD9QvHzMwaQbFmpafJ05xEsuBPRMS2NYvKzMwyVezKYZt6BmJmZo2j2JXDDsXeGBF/rn44ZmbWCIrdkF5F0qykPNsCeHlNIjIzs8wVa1bas56BmJlZ4yhlDWlJep+kz6avd5P0utqHZmZmWSllVtZvAK8H3pu+fhr4es0iMjOzzJUyt9IBEbGfpNUAEfGkpGqsIW1mZg2qlCuHEUkdpGMeJO1EMs22mZm1qFKSw1dJZmV9abrm88+Bf6lpVGZmlqlSZmW9QNIq4BCSbq2LI+I3NY/MzMwyU8o9ByLibl5cxtPMzFpcKc1KZmbWZpwczMxsAicHMzObwMnBzMwmcHIwM7MJMkkOkj4v6Q5JayRdJ2l2Wr63pF9Kek7Sp7OIzczMsrtyWB4R+0bEPOAq4NS0/M/Ax4AvZhSXmZmRUXKIiL/kvNyadGqOiHgsIm4DRrKIy8zMEorIt0x0HU6cTMXxAeApYFFEPJ6z7XRgKCIKXkFIWgIsAZg1a9aCFStWVBzT0NAQM2fOrPg4jaQV6wStWS/XqXm0Sr0WLVq0KiJ6822rWXKQdAOwc55NJ0fE5Tn7LQO2iojTcspOZ5LkkKu3tzdWrlxZYcTQ39/PwoULKz5OI2nFOkFr1st1ah6tUi9JBZNDSdNnlCMiDi1x1wuAa4DTJtvRzMzqI6veSnvlvDwKz9tkZtZQanblMImzJc0hWRfiQeBEAEk7AyuBbYGNkj4BvHrcDWwzM6uxTJJDRBxToPwRYJc6h2NmZuN4hLSZmU3g5GBmZhM4OZiZ2QRODmZmNoGTg5mZTeDkYGZmEzg5mJnZBFkNgjMza3t9qwdYfu16Hh4cZnZ3F0sPm8Pi+T1ZhwU4OZiZZaJv9QBLL17LyMZk8tOBwWGWXrwWoCEShJuVzMwycPoVd21KDGNGNganX3FXRhFtzlcOZmZlqqRZaHA4/5pmhcrrzcnBzKwMfasHWHbpOoZHRoGkWWjZpeuAxmgWqpSblczMyrD82vWbEsOY4ZFRll+7vqT3bz+jc0rl9ebkYGZWhocHh6dUPt5pR+xDZ4c2K+vsEKcdsU/FsVWDk4OZWRlmd3dNqXy8xfN7OHb/XelQkiA6JI7df9eGaZJycjAzK8PSw+bQ1dmxWVlXZwdLD5tT0vv7Vg9wyaoBRiPpsTQawSWrBuhbPVD1WMvh5GBmVobF83s46+i59HR3IaCnu4uzjp5b8jf/Su9Z1Jp7K5mZlWnx/J6ym4EqvWdR69HVvnIwM8tAJfcsxrrRDgwOE7zYjbaaTVJODmZmGajknkU9mqTcrGRmloGxJqBymoYqbZIqRSbJQdLngaOAjcBjwPER8bCk44B/BgQ8DXw4IvckM3wAAAkrSURBVNZmEaOZWa2Ve89idncXA3kSQandaEuRVbPS8ojYNyLmAVcBp6bl9wNvioi5wOeB8zKKz8ysYVXajbYUmVw5RMRfcl5uDURa/ouc8luBXeoZl5lZM6ikSapUiojJ96oBSV8APgA8BSyKiMfHbf80sHdEfKjA+5cASwBmzZq1YMWKFRXHNDQ0xMyZMys+TiNpxTpBa9bLdWoejVCvweERHn3qWZ4f3cj0jmnM2m4rurumNi/TokWLVkVEb75tNUsOkm4Ads6z6eSIuDxnv2XAVhFxWk7ZIuAbwBsi4onJztXb2xsrV66sOOb+/n4WLlxY8XEaSSvWCVqzXq5T88i6XuNnhIWkWWkqg/AAJBVMDjVrVoqIQ0vc9QLgGuA0AEn7Av8BvK2UxGBmlpWslvks1pW1WufP5Ia0pL1yXh4F3J2W7wZcCrw/Iu7JIjYzs1LUYyBaIfl6KhUrL0dWvZXOlnSnpDuAtwAfT8tPBXYEviFpjaTK24rMzGogy7mRxmZyLbW8HFn1VjqmQPmHgLw3oM3MGkk9BqIVMlrgXnGh8nJ4+gwzszJUup5DJXoKnKNQeTmcHMzMylCPgWhZnttzK5mZlaEeA9GyPLeTg5lZmSpZz6HRz+1mJTMzm8DJwczMJnByMDOzCZwczMxsAicHMzObILMpu6tJ0uPAg1U41EuAP1XhOI2kFesErVkv16l5tEq9do+InfJtaInkUC2SVhaavrZZtWKdoDXr5To1j1atVy43K5mZ2QRODmZmNoGTw+bOyzqAGmjFOkFr1st1ah6tWq9NfM/BzMwm8JWDmZlN4ORgZmYTtEVykPRWSesl/U7SSXm27y7pRkl3SOqXtEvOtv+WNCjpqvpGPbly6yVpnqRfSror3XZs/aPPr4I67S7p9nR52bsknVj/6Aur5G8w3b6tpD9IOrd+URdX4f+r0fR3tUbSFfWNvLAK67SbpOsk/UbSryXtUc/Yqy4iWvoBdAD3Ai8HpgNrgVeP2+di4O/S5wcDP8zZdghwBHBV1nWpVr2AVwF7pc9nA38Eupu8TtOBLdPnM4EHgNlZ16kaf4Np2VeAHwHnZl2fatQJGMq6DjWoUz/w5py/wRlZ16mSRztcObwO+F1E3BcRzwMrgKPG7fNq4Kb0+c252yPiRuDpegQ6RWXXKyLuiYjfps8fBh4D8o6SrLNK6vR8RDyXlm9JY10VV/Q3KGkBMAu4rg6xlqqiOjWosusk6dXAFhFxPUBEDEXEhvqEXRuN9B+oVnqAh3Je/yEty7UWODp9/jfANpJ2rENslahKvSS9juRb0r01inMqKqqTpF0l3ZEe41/TxNcIyq6XpGnAOcCnax7l1FT697eVpJWSbpW0uLahlqySOr0KGJR0qaTVkpZL6qCJtUNyKMWngTdJWg28CRgARrMNqSqK1kvSy4AfAidExMZsQpyygnWKiIciYl/glcDfSZqVXZhTVqheHwGuiYg/ZBlcmYr9/e0eyfQT7wW+LOkVGcU4VYXqtAXw1+n2/Umapo7PKMaqaIdlQgeAXXNe75KWbZJ+wzwaQNJM4JiIGKxbhOWpqF6StgWuBk6OiFvrEvHkqvK7ioiHJd1J8p/1xzWNuDRl10vS64G/lvQRknbs6ZKGImLCzdI6q+h3FRED6c/7JPUD88n+6rWS39MfgDURcV+6rQ84EPhOPQKvhXa4crgN2EvSnpKmA+8GNusdIekl6eU7wDLgu3WOsRxl1yvd/zLgBxHRCB+eYyqp0y6SutLn2wNvANbXLfLiyq5XRBwXEbtFxB4k30p/0ACJASr7XW0vacuxfYCDgF/XLfLCKvmsuA3oljR27+5gGqNO5cv6jng9HsDbgXtIvpmcnJZ9Djgyff5O4LfpPv9B2usl3fYz4HFgmKQN8rCs61NpvYD3ASPAmpzHvKzrU2Gd3gzcQdImfAewJOu6VOtvMOcYx9MgvZUq/F39H2Bd+rtaB3ww67pU4/eU8ze4DvhPYHrW9ank4ekzzMxsgnZoVjIzsylycjAzswmcHMzMbAInBzMzm8DJwczMJnBysJYmaWiS7d3pALOx17Ml/Th9Pk/S28s45+mSpjTdhaTFkk4tsK1oHSY57hclHVzu+619OTlYu+smmaICSEbARsQ705fzSPq918NngG/U4LhfAxph0Jw1GScHawuSZqbz8N8uaZ2ksdk2zwZeka4rsFzSHpLuTEfIfg44Nt127PgrgnS/PdLnJ0u6R9LPgTk5+7xCyZogqyT9TNLeeWJ7FfBcRPwpfb2nkvU21kk6c9y+SyXdpmQ9gTNyyj+rZB2Cn0u6cCzOiHgQ2FHSzlX5h7S24eRg7eJZ4G8iYj9gEXCOJJF8q743IuZFxNKxnSOZsvlU4KJ020WFDqxkSu138+KVxv45m88DPhoRC0imv8h3dXAQcHvO668A/x4Rc0nW2hg7z1uAvUimlp4HLJD0Rkn7A8cArwXeBvSOO/7t6TnMStYOE++ZAQj4F0lvBDaSTMVcrVlb/xq4LNL5+5WubJZOzPZ/gIuTPAQka02M9zKSKVrGHETyYQ/JrLn/mj5/S/pYnb6eSZIstgEuj4hngWclXTnu+I+RLOpkVjInB2sXx5EsaLQgIkYkPQBsNcVjvMDmV9uTvX8aMBgR8ybZbxjYblxZvnltBJwVEd/arFD6xCTH3yo9h1nJ3Kxk7WI74LE0MSwCdk/Lnyb55p3P+G0PAPsBSNoP2DMt/ymwWFKXpG1IlpUlIv4C3C/pXel7JOm1ec7zG5I1KMbcQtJMBUlSG3Mt8H/TKxIk9Uh6abr/EZK2SrcdPu74rwLuLFBHs7ycHKxdXAD0SloHfAC4GyAingBuSW8uLx/3npuBV4/dkAYuAXaQdBfw/0hm5iQibgcuIpll9Cck0zePOQ74oKS1wF3kXyrzp8B8vdj29HHgH9NYN61EFhHXkawj/ct024+BbSLiNpKppe9Iz78OeApAUidJ4lk5lX8sM8/KatYAJH0FuDIibijz/TMjYkjSDJJksyQibpf0N8B+EfHZasZrrc9XDmaN4V+AGRW8/zxJa0h6Jl2SXs1Acl/xnEqDs/bjKwczM5vAVw5mZjaBk4OZmU3g5GBmZhM4OZiZ2QRODmZmNsH/B3OlF9g4BJavAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "bathymetry production -- stumpf algorithm for ellipsoidal height of sea floor"
      ],
      "metadata": {
        "id": "GErNeKDaBiqL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_ST = (trainphoton[['lnB2perlnB3']].values).reshape((-1,1)) \n",
        "y = (trainphoton[['photon height']].values)\n",
        "model_sdb_st = LinearRegression().fit(x_ST,y)\n",
        "print('Stumpf', model_sdb_st.score(x_ST,y), model_sdb_st.intercept_, float(model_sdb_st.coef_) )\n",
        "plt.scatter(x_ST, y, color = 'black')\n",
        "plt.plot(x_ST, model_sdb_st.predict(x_ST), color = 'blue', linewidth = 3)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "fuC9eZ5WwpO6",
        "outputId": "f69da360-640a-4c29-a633-50fda923c6b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stumpf 0.8033893214070721 [63.76122392] -98.71579611578942\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SDB_ST = ((image.select('lnB2perlnB3')).multiply(float(model_sdb_st.coef_))).add(float(model_sdb_st.intercept_))  \n",
        "#SDB_ST = m0 + m1((ln1000B2)/(ln1000B3))\n",
        "image = image.addBands(SDB_ST.rename('SDB_ST'))\n",
        "print('Stumpf finish ....')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uVSaPeTgyBSH",
        "outputId": "1b650079-fdc0-4ecf-ecba-359bdd89ece1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stumpf finish ....\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "############### VISUALIZATION ############################## \n",
        "folium.Map.add_ee_layer = add_ee_layer\n",
        "c = (AOI.centroid().getInfo())['coordinates']\n",
        "map_matched = folium.Map(location=[c[1], c[0]], zoom_start=12)\n",
        "\n",
        "depth_palette = ['blue','cyan', 'green','orange', 'red']\n",
        "\n",
        "map_matched.add_ee_layer(image,{'min':0, 'max':0.3, 'bands':['B4','B3','B2']},'RGB_L2A')\n",
        "map_matched.add_ee_layer(image,{'min':-36, 'max':-26, 'bands':['SDB_ST'], 'palette' : depth_palette},'h')\n",
        "\n",
        "display(map_matched.add_child(folium.LayerControl()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "7SRxkCe3yKl1",
        "outputId": "733e96ad-4724-4eae-936b-b6cbe03bdda1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<folium.folium.Map at 0x7f56d18418d0>"
            ],
            "text/html": [
              "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
              "&lt;head&gt;    \n",
              "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
              "    &lt;script&gt;L_PREFER_CANVAS=false; L_NO_TOUCH=false; L_DISABLE_3D=false;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css&quot;/&gt;\n",
              "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
              "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
              "    \n",
              "    &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
              "        initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
              "    &lt;style&gt;#map_881c2d607a9e3da4d9b4a101ac0f6e84 {\n",
              "        position: relative;\n",
              "        width: 100.0%;\n",
              "        height: 100.0%;\n",
              "        left: 0.0%;\n",
              "        top: 0.0%;\n",
              "        }\n",
              "    &lt;/style&gt;\n",
              "&lt;/head&gt;\n",
              "&lt;body&gt;    \n",
              "    \n",
              "    &lt;div class=&quot;folium-map&quot; id=&quot;map_881c2d607a9e3da4d9b4a101ac0f6e84&quot; &gt;&lt;/div&gt;\n",
              "&lt;/body&gt;\n",
              "&lt;script&gt;    \n",
              "    \n",
              "    \n",
              "        var bounds = null;\n",
              "    \n",
              "\n",
              "    var map_881c2d607a9e3da4d9b4a101ac0f6e84 = L.map(\n",
              "        &#x27;map_881c2d607a9e3da4d9b4a101ac0f6e84&#x27;, {\n",
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              "        &#x27;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&#x27;,\n",
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              "        &quot;tms&quot;: false\n",
              "}).addTo(map_881c2d607a9e3da4d9b4a101ac0f6e84);\n",
              "    var tile_layer_c6a1c486b371da9b888beb79fa60b4a8 = L.tileLayer(\n",
              "        &#x27;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/d22749886dbb496c76c1d8165354d340-7cc11fada1c334f3b2e512ea373a4107/tiles/{z}/{x}/{y}&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: &quot;Map Data &amp;copy; &lt;a href=\\&quot;https://earthengine.google.com/\\&quot;&gt;Google Earth Engine&lt;/a&gt;&quot;,\n",
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              "}).addTo(map_881c2d607a9e3da4d9b4a101ac0f6e84);\n",
              "    \n",
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              "                 autoZIndex: true\n",
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              "            \n",
              "        \n",
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "try convert to orthometric height ..."
      ],
      "metadata": {
        "id": "K1omGqTtB3e1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def MSL(vec):\n",
        "    lat,lon,h = vec\n",
        "    geoid_model = '/content/drive/MyDrive/SeniorProject/tgm2017-1.pgm'    # <---- get your geoid model file\n",
        "    geoid_interpolator = pygeodesy.GeoidKarney(geoid_model)\n",
        "    # Get the geoid height\n",
        "    pos = LatLon(lat, lon)\n",
        "    N = geoid_interpolator(pos)\n",
        "    return h-N\n",
        "trainphoton['H'] = trainphoton[['latitude', 'longitude', 'photon height']].apply(MSL, axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zKsyI4Mx0PnG",
        "outputId": "50d7213c-f888-4fe9-bdc1-4341aa5caa2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  if __name__ == '__main__':\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainphoton"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "BmvekLA20dGj",
        "outputId": "c6487772-a503-490c-a68b-0e2cedff9923"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        latitude   longitude  photon height  confidence code  lnB2perlnB3  \\\n",
              "32772  12.614146  101.378301     -27.703670                4     0.917257   \n",
              "32816  12.614064  101.378292     -27.048449                4     0.914222   \n",
              "32853  12.613981  101.378283     -27.056513                4     0.917256   \n",
              "32878  12.613949  101.378280     -27.111288                4     0.917256   \n",
              "32879  12.613949  101.378280     -27.111906                4     0.917256   \n",
              "...          ...         ...            ...              ...          ...   \n",
              "32667  12.614306  101.378318     -25.762090                4     0.917893   \n",
              "32671  12.614300  101.378317     -25.826885                4     0.917893   \n",
              "32701  12.614261  101.378313     -25.799225                4     0.917893   \n",
              "32766  12.614166  101.378303     -25.822392                4     0.917257   \n",
              "32811  12.614070  101.378293     -25.842790                4     0.914222   \n",
              "\n",
              "              H  \n",
              "32772 -1.722467  \n",
              "32816 -1.067378  \n",
              "32853 -1.075574  \n",
              "32878 -1.130400  \n",
              "32879 -1.131018  \n",
              "...         ...  \n",
              "32667  0.219371  \n",
              "32671  0.154566  \n",
              "32701  0.182164  \n",
              "32766  0.158842  \n",
              "32811  0.138292  \n",
              "\n",
              "[96 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3e00b96e-a958-4d95-82b4-c015c4096c66\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>photon height</th>\n",
              "      <th>confidence code</th>\n",
              "      <th>lnB2perlnB3</th>\n",
              "      <th>H</th>\n",
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              "      <th>32772</th>\n",
              "      <td>12.614146</td>\n",
              "      <td>101.378301</td>\n",
              "      <td>-27.703670</td>\n",
              "      <td>4</td>\n",
              "      <td>0.917257</td>\n",
              "      <td>-1.722467</td>\n",
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              "    <tr>\n",
              "      <th>32816</th>\n",
              "      <td>12.614064</td>\n",
              "      <td>101.378292</td>\n",
              "      <td>-27.048449</td>\n",
              "      <td>4</td>\n",
              "      <td>0.914222</td>\n",
              "      <td>-1.067378</td>\n",
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              "    <tr>\n",
              "      <th>32853</th>\n",
              "      <td>12.613981</td>\n",
              "      <td>101.378283</td>\n",
              "      <td>-27.056513</td>\n",
              "      <td>4</td>\n",
              "      <td>0.917256</td>\n",
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              "      <th>32878</th>\n",
              "      <td>12.613949</td>\n",
              "      <td>101.378280</td>\n",
              "      <td>-27.111288</td>\n",
              "      <td>4</td>\n",
              "      <td>0.917256</td>\n",
              "      <td>-1.130400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32879</th>\n",
              "      <td>12.613949</td>\n",
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              "      <td>-27.111906</td>\n",
              "      <td>4</td>\n",
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              "      <td>-1.131018</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32667</th>\n",
              "      <td>12.614306</td>\n",
              "      <td>101.378318</td>\n",
              "      <td>-25.762090</td>\n",
              "      <td>4</td>\n",
              "      <td>0.917893</td>\n",
              "      <td>0.219371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32671</th>\n",
              "      <td>12.614300</td>\n",
              "      <td>101.378317</td>\n",
              "      <td>-25.826885</td>\n",
              "      <td>4</td>\n",
              "      <td>0.917893</td>\n",
              "      <td>0.154566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32701</th>\n",
              "      <td>12.614261</td>\n",
              "      <td>101.378313</td>\n",
              "      <td>-25.799225</td>\n",
              "      <td>4</td>\n",
              "      <td>0.917893</td>\n",
              "      <td>0.182164</td>\n",
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              "      <th>32766</th>\n",
              "      <td>12.614166</td>\n",
              "      <td>101.378303</td>\n",
              "      <td>-25.822392</td>\n",
              "      <td>4</td>\n",
              "      <td>0.917257</td>\n",
              "      <td>0.158842</td>\n",
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              "    <tr>\n",
              "      <th>32811</th>\n",
              "      <td>12.614070</td>\n",
              "      <td>101.378293</td>\n",
              "      <td>-25.842790</td>\n",
              "      <td>4</td>\n",
              "      <td>0.914222</td>\n",
              "      <td>0.138292</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>96 rows × 6 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e00b96e-a958-4d95-82b4-c015c4096c66')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-3e00b96e-a958-4d95-82b4-c015c4096c66 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "photonplot(trainphoton['lnB2perlnB3'],trainphoton['H'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "K6tW_CmW0mMk",
        "outputId": "b01507ec-7700-45aa-fbb6-6a06e4db5a82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "bathymetry production -- stumpf algorithm for orthometric height of sea floor"
      ],
      "metadata": {
        "id": "vI7xBZ-qB0Jm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_ST = (trainphoton[['lnB2perlnB3']].values).reshape((-1,1)) \n",
        "H = (trainphoton[['H']].values)\n",
        "model_sdb_st = LinearRegression().fit(x_ST,H)\n",
        "print('Stumpf', model_sdb_st.score(x_ST,H), model_sdb_st.intercept_, float(model_sdb_st.coef_) )\n",
        "plt.scatter(x_ST, H, color = 'black')\n",
        "plt.plot(x_ST, model_sdb_st.predict(x_ST), color = 'blue', linewidth = 3)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "lCamsU-40sj0",
        "outputId": "3be04217-72ac-4e77-a333-598d6970722e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stumpf 0.8033893125510205 [89.78001397] -98.75695642014475\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SDB_ST_Depth = ((image.select('lnB2perlnB3')).multiply(float(model_sdb_st.coef_))).add(float(model_sdb_st.intercept_))  \n",
        "#SDB_ST = m0 + m1((ln1000B2)/(ln1000B3))\n",
        "image = image.addBands(SDB_ST_Depth.rename('SDB_ST_HH'))\n",
        "print('Stumpf finish ....')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3PtRjEa-01yZ",
        "outputId": "04d8067a-d927-49c6-cf72-2aae72e9a6a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stumpf finish ....\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "############### VISUALIZATION ############################## \n",
        "folium.Map.add_ee_layer = add_ee_layer\n",
        "c = (AOI.centroid().getInfo())['coordinates']\n",
        "map_matched = folium.Map(location=[c[1], c[0]], zoom_start=12)\n",
        "\n",
        "depth_palette = ['blue','cyan', 'green','orange', 'red']\n",
        "\n",
        "map_matched.add_ee_layer(image,{'min':0, 'max':0.3, 'bands':['B4','B3','B2']},'RGB_L2A')\n",
        "map_matched.add_ee_layer(image,{'min':-30, 'max':0, 'bands':['SDB_ST_HH'], 'palette' : depth_palette},'h')\n",
        "\n",
        "display(map_matched.add_child(folium.LayerControl()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "Q0tMlI0s1G04",
        "outputId": "079f002b-7b18-4e7d-8ea4-aaa6d60532d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<folium.folium.Map at 0x7f56d1831050>"
            ],
            "text/html": [
              "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
              "&lt;head&gt;    \n",
              "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
              "    &lt;script&gt;L_PREFER_CANVAS=false; L_NO_TOUCH=false; L_DISABLE_3D=false;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css&quot;/&gt;\n",
              "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
              "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
              "    \n",
              "    &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
              "        initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
              "    &lt;style&gt;#map_34a040fb592ffa1e7df4641b9e31c83a {\n",
              "        position: relative;\n",
              "        width: 100.0%;\n",
              "        height: 100.0%;\n",
              "        left: 0.0%;\n",
              "        top: 0.0%;\n",
              "        }\n",
              "    &lt;/style&gt;\n",
              "&lt;/head&gt;\n",
              "&lt;body&gt;    \n",
              "    \n",
              "    &lt;div class=&quot;folium-map&quot; id=&quot;map_34a040fb592ffa1e7df4641b9e31c83a&quot; &gt;&lt;/div&gt;\n",
              "&lt;/body&gt;\n",
              "&lt;script&gt;    \n",
              "    \n",
              "    \n",
              "        var bounds = null;\n",
              "    \n",
              "\n",
              "    var map_34a040fb592ffa1e7df4641b9e31c83a = L.map(\n",
              "        &#x27;map_34a040fb592ffa1e7df4641b9e31c83a&#x27;, {\n",
              "        center: [12.580315013034324, 101.40741500000003],\n",
              "        zoom: 12,\n",
              "        maxBounds: bounds,\n",
              "        layers: [],\n",
              "        worldCopyJump: false,\n",
              "        crs: L.CRS.EPSG3857,\n",
              "        zoomControl: true,\n",
              "        });\n",
              "\n",
              "\n",
              "    \n",
              "    var tile_layer_bfa65cde61d9a608f35b5ee2a3844d93 = L.tileLayer(\n",
              "        &#x27;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: null,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 0,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_34a040fb592ffa1e7df4641b9e31c83a);\n",
              "    var tile_layer_7453095039bc19f564aff7560a2a316b = L.tileLayer(\n",
              "        &#x27;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/034829d09dada9325055be9d48d3eafc-ff48fe647a781baa32479653c5f4f638/tiles/{z}/{x}/{y}&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: &quot;Map Data &amp;copy; &lt;a href=\\&quot;https://earthengine.google.com/\\&quot;&gt;Google Earth Engine&lt;/a&gt;&quot;,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 0,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_34a040fb592ffa1e7df4641b9e31c83a);\n",
              "    var tile_layer_327cf79031cd702950d42cb95d420d76 = L.tileLayer(\n",
              "        &#x27;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/94a26144f58d3ee18b395f28115af2db-bd0e5ca9c859b229d8b932abede4bf4e/tiles/{z}/{x}/{y}&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: &quot;Map Data &amp;copy; &lt;a href=\\&quot;https://earthengine.google.com/\\&quot;&gt;Google Earth Engine&lt;/a&gt;&quot;,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 0,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_34a040fb592ffa1e7df4641b9e31c83a);\n",
              "    \n",
              "            var layer_control_e85debffb99a4037f085c9d32cc7452e = {\n",
              "                base_layers : { &quot;openstreetmap&quot; : tile_layer_bfa65cde61d9a608f35b5ee2a3844d93, },\n",
              "                overlays : { &quot;RGB_L2A&quot; : tile_layer_7453095039bc19f564aff7560a2a316b,&quot;h&quot; : tile_layer_327cf79031cd702950d42cb95d420d76, }\n",
              "                };\n",
              "            L.control.layers(\n",
              "                layer_control_e85debffb99a4037f085c9d32cc7452e.base_layers,\n",
              "                layer_control_e85debffb99a4037f085c9d32cc7452e.overlays,\n",
              "                {position: &#x27;topright&#x27;,\n",
              "                 collapsed: true,\n",
              "                 autoZIndex: true\n",
              "                }).addTo(map_34a040fb592ffa1e7df4641b9e31c83a);\n",
              "            \n",
              "        \n",
              "&lt;/script&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainphoton.to_csv('traindepth.csv')"
      ],
      "metadata": {
        "id": "xi1JyelW4gtQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sdb_band = image.select('SDB_ST_HH')\n",
        "\n",
        "task = ee.batch.Export.image.toDrive(**{\n",
        "    'image': sdb_band,\n",
        "    'description': 'icesatonly',\n",
        "    'folder':'Example_folder',\n",
        "    'scale': 10,\n",
        "    'region': AOI.getInfo()['coordinates']\n",
        "})\n",
        "task.start()\n",
        "\n",
        "import time \n",
        "while task.active():\n",
        "  print('Polling for task (id: {}).'.format(task.id))\n",
        "  time.sleep(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HH-Cqmug15Kd",
        "outputId": "2edeec80-d06b-4e17-fd01-99e4e08e9b3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n",
            "Polling for task (id: YHRDHVAMUTS52Z5SGGUB5WLB).\n"
          ]
        }
      ]
    }
  ]
}